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Elahe Dabaghi, Keyvan Molanourozi, Seyed Kazem Mousavi Sadati, Abdollah Ghasemi,
Volume 0, Issue 0 (3-2024)
Volume 0, Issue 0 (3-2024)
Abstract
Background & Aims: The purpose of this study was to investigate the effectiveness of a session of coach, mirror and self-control observation on learning the movement sequence of form movements using Kinect sensor evaluation.
Methods: The number of 30 novice women and 30 skilled women in form movements with the age range of 18 to 30 were selected as available and each category was randomly divided into three groups of 10 people: direct observation of the coach, observation of the coach in the mirror, self-control observation. The exercises consisted of a one-hour training session of new form movements from Kinect's set of rhythmic movements, which were taught to the participants by three methods: direct observation, mirror and self-control. The evaluation of the performance of the movement sequence in the stages of acquisition, acquisition, retention, and follow-up was evaluated through the Kinect sensor (highest score = 100).
Results: The results showed that all groups showed the significant acquisition (M > 50; P ≤ 0.05) and their performance in retention and follow-up tests was maintained (P > 0.05). Skilled groups performed significantly better than novice groups (P≤0.05). Also, the self-control groups performed better in all stages of evaluation than other groups (P≤0.05).
Conclusion: Therefore, self-control of observation exercises improves learning a new movement sequence in form movement learners.
Methods: The number of 30 novice women and 30 skilled women in form movements with the age range of 18 to 30 were selected as available and each category was randomly divided into three groups of 10 people: direct observation of the coach, observation of the coach in the mirror, self-control observation. The exercises consisted of a one-hour training session of new form movements from Kinect's set of rhythmic movements, which were taught to the participants by three methods: direct observation, mirror and self-control. The evaluation of the performance of the movement sequence in the stages of acquisition, acquisition, retention, and follow-up was evaluated through the Kinect sensor (highest score = 100).
Results: The results showed that all groups showed the significant acquisition (M > 50; P ≤ 0.05) and their performance in retention and follow-up tests was maintained (P > 0.05). Skilled groups performed significantly better than novice groups (P≤0.05). Also, the self-control groups performed better in all stages of evaluation than other groups (P≤0.05).
Conclusion: Therefore, self-control of observation exercises improves learning a new movement sequence in form movement learners.
Ali Tayebi, Shabahang Jafarnejad, Mahya Naderkhani, Ali Arjmand Tajoddini, Seyede Fateme Nouri Abhari, Faranak Olamaeian, Shahin Keshtkar Rajabi, Alireza Zakaryaei,
Volume 0, Issue 0 (3-2024)
Volume 0, Issue 0 (3-2024)
Abstract
Background:
An emerging disease surfaced in China in December 2019, manifesting as acute respiratory syndrome, rapidly evolving into a global pandemic (1-3). The cause was identified as the severe acute respiratory syndrome coronavirus (SARS-CoV-2), designated COVID-19 by the World Health Organization on January 30, 2020 (4). In response to its high fatality rate and contagiousness, countries worldwide implemented widespread closures, including Iran, where all administrative, commercial, and educational activities ceased. Consequently, virtual education gained prominence to mitigate academic setbacks and ensure continuity (5). This shift necessitated educators, many inexperienced in virtual teaching, to adapt quickly (6). Medical education also adapted to these conditions, with significant impacts on student learning in hospital settings. Face-to-face teaching in hospitals and clinics was curtailed due to the disease burden in Iran, leading to substantial changes in medical education (7, 8). Studies in Iran have yielded conflicting findings on the pandemic's effects on medical education, highlighting both the efficacy of online and virtual medical courses and varying outcomes (8-12). Recognizing the significance of the semiology course for medical students, crucial for learning clinical examinations and patient interaction skills before entering hospital departments, emphasizes the need for structured training in semiology. This foundational unit underpins medical and clinical education, playing a vital role in future patient care and healthcare management (13). The current research was designed with the aim of investigating the level of students' satisfaction and students' views on different aspects of practical semiology unit courses in two groups of students who completed the course in person and virtual.
Methods:
This study was a meticulous examination conducted among medical students enrolled at Iran University of Medical Sciences (IUMS) in Tehran throughout the year 2022. To ensure ethical standards, the study commenced following approval from the IUMS Ethics and Research Committee (approval number: IR.IUMS.REC.1401.490). Participants in this study were enrolled using convenience sampling into two groups: a virtual learning group and a face-to-face learning group, both meticulously matched in demographic characteristics such as age, gender, and academic semester. The sampling strategy employed convenience sampling, categorizing participants into two distinct groups: face-to-face and virtual learning environments. This stratification aimed to maintain demographic equilibrium concerning variables such as age, gender, and academic semester, thereby mitigating potential confounding factors. Adequate sample sizes were computed to uphold statistical power, mandating a minimum of 193 participants encompassing both groups: 65 in the virtual cohort and 128 in the face-to-face cohort. The practical semiology curriculum was tailored based on specific needs identified among students at Iran University of Medical Sciences (IUMS). For the virtual course, educational content included 57 carefully crafted instructional videos sourced from expert lectures and practical demonstrations. These resources were made available on the IUMS online platform prior to the course commencement, ensuring accessibility and preparation for virtual learners. Conversely, the face-to-face course was conducted in a spacious, well-ventilated facility equipped with 15 dedicated learning stations. Each station accommodated 5-9 students, an instructor, and occasionally a simulated patient actor when required. These stations were fully stocked with essential tools and educational aids, facilitating hands-on learning experiences under direct educator supervision. The curriculum spanned three intensive modules over 12 days, covering critical topics such as clinical history-taking, physical examinations, and technical competencies including surgical techniques and invasive procedures. Outcome measures centered on evaluating student perceptions of the learning environment and overall satisfaction levels. This assessment was conducted using the Dundee Ready Education Environment Measure (DREEM) (14) questionnaire, a validated tool designed to assess educational climate effectiveness across domains like learning, teaching quality, academic self-perception, educational atmosphere, and social conditions. The DREEM questionnaire assesses students' perceptions across several dimensions: learning, teachers, academic ability, educational atmosphere, and social conditions of education. Each component uses a 5-point Likert scale, with scores categorized into poor, moderate, and high levels based on predefined score ranges. Additionally, student satisfaction with the semiology course is evaluated through a separate 10-question online questionnaire, also using a 5-point Likert scale, with scores classified into poor, moderate, and high satisfaction levels. Statistical analysis of collected data utilized SPSS software, employing standard metrics such as mean ± standard deviation for continuous variables and percentages for categorical data. Comparative analysis included Mann-Whitney U tests, independent sample t-tests, and chi-square tests as appropriate, with significance set at P ≤ 0.05. These rigorous analytical approaches aimed to discern significant differences and draw robust conclusions regarding the efficacy of each educational modality in fostering student learning and satisfaction in semiology training at IUMS.
Results:
A total of 325 medical students were included in this study. 22.8% of the students (n=74) had participated in virtual training and 77.2% of the students (n=251) had participated in the face-to-face semiology course. The average age of students was 22.65 ± 2.75 years and ranged from 20 to 38 years (P=0.8). 54.2% (n=176) were male students and 45.8% (n=149) were female students (P=0.01) (Table 1).
Table 1. Demographic Characteristics of the Participants by Type of Learning Groups.
| Characteristics | face-to-face learning group (n=251) |
Virtual learning group (n=74) |
P value[1] |
| Gender, n (%) Male Female |
127 (50.6%) 124 (49.4%) |
49 (66.2%) 25 (33.8%) |
0.01 |
| Age, years (SD)[2] | 22.49 (2.4) | 22.97 (2.9) | 0.8 |
The relationship between the type of students' training course (face-to-face or virtual) and their level of satisfaction was investigated. Statistical analysis did not show a significant relationship between these two variables (P=0.1) (Table 2).
Table 2. Satisfaction of the Participants by Type of Learning Groups.
| Characteristics | face-to-face learning group (n=251) |
Virtual learning group (n=74) |
P value[3] |
| Satisfaction, n (%) Poor Moderate High |
0 (0) 16 (6.4) 235 (93.6) |
1 (1.3) 7 (9.5) 66 (89.2) |
0.1 |
The dimensions of the DREEM questionnaire were examined, Overall, poor perception had a very low prevalence (0-1.4%). In contrast, high perception in students showed a prevalence of more than 90% in all dimensions in face-to-face education and between 77 and 91.9% in virtual education. Except for SPSCE, in other dimensions, the level of high perception was significantly higher in students with face-to-face education (P<0.05) (Table 3).
Table 3. Examining and Comparing the Dimensions of Students' Perception by Type of Learning Groups.
| Characteristics | face-to-face learning group (n=251) |
Virtual learning group (n=74) |
P value3 |
| Students' perception of learning, n (%) Poor Moderate High |
1 (0.4) 23 (9.2) 227 (90.4) |
0 (0) 17 (23) 57 (77) |
0.006 |
| Students' perception of teachers, n (%) Poor Moderate High |
0 (0.4) 12 (4.8) 239 (95.2) |
0 (0) 14 (18.9) 60 (81.1) |
0.001> |
| Students' perception of their academic ability, n (%) Poor Moderate High |
0 (0) 13 (5.2) 238 (94.8) |
1 (1.4) 11 (14.9) 62 (83.8) |
0.003 |
| Students' perception of educational atmosphere, n (%) Poor Moderate High |
0 (0) 8 (3.2) 243 (96.8) |
1 (1.4) 8 (10.8) 65 (87.8) |
0.005 |
| Students' perception of the social conditions of education, n (%) Poor Moderate High |
0 (0) 14 (5.6) 237 (94.4) |
0 (0) 6 (8.1) 68 (91.9) |
0.4 |
Conclusion:
In general, according to the DREEM index, the level of satisfaction with education in general and in all its dimensions was reported to be high and suitable for both learning groups. The overall score of satisfaction and perception with education based on DREEM, for a group of students who participated in face-to-face learning, in all dimensions including: SPL, SPT, SPAA, SPEA and SPSCE was more than the virtual group. This study showed that although virtual training has high satisfaction among students, the face-to-face training method caused higher academic satisfaction and perception. Therefore, considering the nature of the semiology course and its clinical importance, it is suggested that this course and, in general, courses that have a high clinical aspect, be taught face-to-face. It is recommended to compare the final test results of students in two face-to-face and virtual groups of semiology course in other studies.
[1] *P-values indicate differences between students by type of learning groups. P < 0.05 was considered statistically significant.
[2] Abbreviation: SD, standard deviation.
[3] *P-values indicate differences between students by type of learning groups. P < 0.05 was considered statistically significant.
A Jamali, L Mokhber-Al-Safa, M Nojoomi,
Volume 10, Issue 37 (3-2004)
Volume 10, Issue 37 (3-2004)
Abstract
Although we have access to various drugs and diagnostic equipment today, tuberculosis is still regarded as the most fatal disease of single microbial factor. A large number of children across the world have been made orphans due to this disease. World Health Organization(WHO) called a global emergency for tuberculosis in 1993. Because of MDR(Multi Drug Resistance) there is a risk that tuberculosis turns to a disease difficult to treat. Among the causes of global defeat in the treatment of tuberculosis and formation of MDR, are delay in treatment and physicians’ faults. Nevertheless, the disease can still be controlled by implementation of DOTS(Directly Observed Short Course). The necessity for organizing such course is the cooperation of all private sectors. One of the plans of this course, based on which national schemes are designed, is educating the medical staff at all levels specially the physicians. This study is aimed at evaluating one of the educational methods on the knowledge and attitudes of general practitioners. This study is based on a before-after study method in which 340 GPS of private sector have been simply sampled randomly. The knowledge and attitudes of the physicians were tested by a questionnaire. A self study method was also used as an educational method, using the book entitled “National Guide for Combating Tuberculosis”. The study proved that the physicians primarily had a favorable attitude to the national plan but their knowledge was very low(1.8%). Education proved to have a positive effect on knowledge and attitudes of the physicians in a way that the mean scores given to physicians’ knowledge and attitudes before and after education showed a significant difference. The change in knowledge and attitudes of physicians of public sector did not show any significant relation with demographic and work experience variant in public sector. Based on the results of the present study it can be concluded that self-learning has a positive effect on attitudes and specially knowledge of the physicians.
M Jadidi, S.m Firoozabadi, A.a Vafaei,
Volume 13, Issue 51 (6-2006)
Volume 13, Issue 51 (6-2006)
Abstract
Background & Aim: In human environment, magnetic fields are created by electrical generators, power lines, and electrical instruments. These fields could affect learning and memory. This study was planned to evaluate whether short time exposure to magnetic fields has any significant effect on spatial memory. Material & Method: In this experimental study, we used 10 male Albino Wistar mice that were trained for spatial memory in a T-maze model within six days. Twenty-four hours after training, animals were tested for retention of discrimination in three stages(control, restrainer and magnetic field) at two-hour intervals and each of the animals was given 5 successive trials at one-minute intervals. The time of movement from the start area until they reached the criterion zone was measured by a chronometer. Magnetic field was induced by a round coil with an internal diameter of 8 cm and 850 turns of copper wire. Maximum intensity of 7.5 mT at the center of the coil was calibrated by a digital teslameter. Results: Evaluation of the results of the three stages indicated that the mean of time was 15.4 s, 11.5 s, 11.3 s respectively. Also, there was a significant difference between the time in the control stage and the other stages (P<0.05), but there was no significant difference between the restrainer stage and the magnetic field regarding the time measured. Conclusion: Research findings indicated that short time 7.5 mT, 50 Hz magnetic field did not have any significant effect on T-maze alternation tasks in mice and it would probably have no effect on spatial memory process either.
M.r. Palizvan, M.r. Khazaee,, M.r. Nakhaee, , E. Ghaznavi Rad,,
Volume 14, Issue 56 (11-2007)
Volume 14, Issue 56 (11-2007)
Abstract
Background & Aim: Impressive research demonstrates the importance of essential fatty acids for many physiological and behavioral mechanisms in both humans and animals. Experimental research showed that deficit in learning and memory is induced after kindling. The objective of this study was to investigate whether dietary cis and trans fatty acids, pre and during pentylenetetraxol(PTZ) kindling would effect learning deficits in male rats. Materials and Methods: This is an experimental research. Eighteen Wistar rats were assigned in three groups. The control group received standard diet. The cis and trans groups received cis and trans fatty acids respectively. The kindling process was initiated one month after the start of the experiment, and the shuttle box learning ability was assessd one week after kindling. Data were analyzed using one way ANOVA and Tukey test. Results: Results showed control and trans groups had impairment in shuttle box learning and no considerable differences were found between these two groups. However on the other hand administration of dietary cis fatty acid reduced impairment in shuttle box learning in pentylenetetrazol kindled rats. Conclusion: The results of this experiment suggest that dietary treatment of kindled rats with cis fatty acids reduced learning deficits induced by PTZ kindling in rats.
M. Roghani,, T. Baluch Nejad Mojarad, , S. Taheri ,
Volume 14, Issue 57 (2-2008)
Volume 14, Issue 57 (2-2008)
Abstract
Background & Aim: Diabetes mellitus accompanies disturbances in learning, memory, and cognitive skills in the human society and experimental animals. There is some evidence for anti-diabetic activity of Vaccinium myrtillus(VM) and the beneficial effect of its fruit on learning and memory in normal animals. Therefore, this research study was conducted to evaluate the effect of chronic oral administration of VM on learning and memory in diabetic rats. Material and Methods: In this experimental study, female Wistar rats(n=36) were divided randomly into four: control, VM-treated control, diabetic, and VM-treated diabetic groups. Treatment groups received rat chow containing 6.25% VM with no restriction for 4 weeks. For induction of diabetes, streptozotocin was injected i.p. at a single dose of 60 mg/kg. For evaluation of learning and memory, initial(IL) and step-through latencies(STL) were determined at the end of study using passive avoidance test and alternation behavior percentage was obtained using Y maze. For statistical analysis, one-way ANOVA and repeated measure ANOVA were used for weight and glucose data and Kruskal-Wallis test was used for behavioral parameters. Results: There was a significant increase in IL in diabetic and VM-treated diabetic groups after 4 weeks as compared to control group(P<0.05). Meanwhile, STL significantly decreased(P<0.05) in diabetic group and significantly increased(P<0.05) in VM-treated diabetic group. In addition, STL did significantly change in VM-treated control group in comparison with control group(P<0.05). Alternation percentage was significantly lower in diabetic group relative to control(P<0.05), treated diabetic group did not show a significant difference in comparison with diabetic group, and VM treatment in control group also did not produce a significant difference as compared to control. Conclusion: VM treatment could enhance the capability of consolidation and recall in diabetic animals.
A. Pourmotabbed,, E. Mehrabi Nasab, F. Soraya, S. Moradi, H. Haghighizad,, M. Tahmassian,,
Volume 15, Issue 0 (6-2008)
Volume 15, Issue 0 (6-2008)
Abstract
Background & Aim: Opiates addiction is a phenomenon with complex physiological and social causes and consequences. The exact mechanisms of development of dependency and relapse remain unclear. Among the several possible mechanisms, the role of learning and memory in opiate dependency and relapse has received considerable attention in recent years. Therefore, in the current study the effect of morphine dependency induced by repeated subcutaneous injection of morphine on the above-mentioned parameters was examined. Material and Method: In this experimental study, animals in both dependent and withdrawal groups received morphine sulfate(10 mg/kg, s.c.) and normal saline was given to the control group. The administration of morphine sulfate and normal saline was done twice per day(8:00 AM & 8:00 PM) for 15 consecutive days. Dependent and control groups were observed from the 11th to 15th day but withdrawal animals were studied from the 16th to the 20th day. The animals were tested for four consecutive days(4 trials/day) to evaluate spatial learning process. During these 4 days(training days) the position of the hidden platform was unchanged. On the fifth day(probe trial) the platform was removed from maze to evaluate spatial memory process. The recorded spatial learning and memory parameters were subjected to ANOVA. Results: The data showed that traveled times and distances to find the hidden platform, the mean of swimming speed on training days, and also the percentage of times and distances traveled in the target quarter in the probe trial stage were not statistically different among the studied groups. Conclusion: These findings clearly imply that morphine dependency induced by the current method has no significant effect on spatial learning and memory, which may result from a relative tolerance to morphine in dependent animals.
Mahsa Ghajarzadeh, Fatemeh Adili-Aghdam,
Volume 19, Issue 94 (4-2012)
Volume 19, Issue 94 (4-2012)
Abstract
Background: Learning is a complex process that many factors such as learning style has key roles on it. The goal of this study was to determine learning style of different field of medical residents.
Methods : By random selection, seven medical fields (radiology, internal medicine, surgery, pediatrics, psychology, ophthalmology and emergency medicine) selected. Sixteen residents from each field were chosen. They asked to fill Kolb learning style Inventory.
Results : The most common learning style among medical residents was assimilator followed by converger.
Conclusion : Considering assimilator and converger learning styles among medical residents is recommended that faculty members should use diagrams, lectures and self-learning methods.
Zahra Kiasalari, Mohsen Khalili, Leyla Ghanbarian,
Volume 19, Issue 95 (5-2012)
Volume 19, Issue 95 (5-2012)
Abstract
Background: Diabetes mellitus is accompanied with disturbances in learning, memory, and cognitive skills in the human society and experimental animals. Considering the potential anti-diabetic effect of the medicinal plant Crocus sativus (Saffron) and the augmenting effect of its consumption on the memory and mental health, this study was conducted to evaluate the effect of chronic interaperitoneal administration of Crocus.S extract on learning and memory in diabetic rats.
Methods: In this experimental study a total of 60 male rats were divided into normal and diabetic groups. Then, each of these groups was divided into three subgroups. Two of these subgroups received 30 and 60 mg/kg crocus.s extract in the treatment periods, but the third group didn’t receive any treatment. At the end of treatment period, each of these subgroups was evaluated by two ways: Y-maze (alternative behavior percentage) and shuttle box (initial latencies (IL) and step-through latencies (STL)) in passive avoidance test. Finally, obtained data were subjected to one way ANOVA test and post hoc Tokey analysis.
Results: Treatment of the diabetic animals with the extract could antagonized the augmenting effect of diabetics on initial latency (P< 0.05). Also, treatment of the diabetic rats with the extract (60 mg/kg) increased the reduced step through latency time (recall of the data) induced by diabetic in the animals (P< 0.05). However, obtained data from Y maze test show that the extract could not improved the spatial memory disruption due to diabetics.
Conclusion: Chronic Crocus.S injection is effective on the capability of maintaining information in the stores memory and reminding in the diabetic rats. However, the extract could not improve the spatial memory in the diabetic rats.
Seyed Kamran Soltani Arabshahi, Leila Naeimi,
Volume 20, Issue 113 (11-2013)
Volume 20, Issue 113 (11-2013)
Abstract
Background: In medical profession,
the ability to set individual learning experiences is very important to become
a successful life-long self-directed learner. Also these skills will help
students acquire knowledge, and kills and unique attitudes in their personal
and professional growth. Therefore to empower the students to achieve
Self-directed learning skills must be one of the ultimate goals of any
educational curriculum. The present study was conducted aiming at investigating
self directed learning skills of medical students (Basic sciences, Pre clinical
period, Clerkship) in Tehran University of Medical Sciences.
Methods: This descriptive – cross
sectional study was undertaken on 182 medical students (basic sciences,
pre-clinical, and clerkship levels) at Tehran University of Medical Sciences.
The instrument used was Fischer’s Self-directed Learning Readiness Scale
(SDLRS) Data were analyzed using SPSS19 software and Descriptive statistics,
independent T-test, One Way ANOVA and cohen’s statistics.
Results: The highest mean among the skills was
associated with self-control (60.6±6.45)
and the lowest was related to the self-management (45.2 6.40)..
Conclusions: Although
self-control skills than other skills, it seems that the learning skills and management
should be included in the curriculum and extracurricular activities, as an
effective step for students to become Self-Directed Lifelong learners.
Dr Robab Sheikhpour, Dr Razieh Sheikhpour,
Volume 23, Issue 144 (6-2016)
Volume 23, Issue 144 (6-2016)
Abstract
Introduction: Breast cancer is the most common cancer in women. An accurate and reliable system for early diagnosis of benign or malignant tumors seems necessary. We can design new methods using the results of FNA and data mining and machine learning techniques for early diagnosis of breast cancer which able to detection of breast cancer with high accuracy. Materials and Methods: In this study, 699 samples of benign and malignancy with 9 characteristics from WBCD and 569 samples of benign and malignancy with 30 characteristics from WDBC were used. Then, a model based on non-parametric kernel density estimation is proposed for classification of WBCD and WDBC data. Results: The results of non parametric methods showed that Gaussian kernel method based on Euclidean distance with accuracy ٪97.93 has the highest accuracy on WDBC data and Gaussian kernel based on Euclidean distance and k-nearest neighbor methods with accuracy ٪98.17 has the highest accuracy compared with other methods on WBCD data for breast cancer disease. Conclusion: The result of this study showed that non-parametric kernel density estimation based classification can be used for breast cancer diagnosis with high accuracy.
Ala Etemad, Zahra Kordloo, Akram Hashemi,
Volume 24, Issue 161 (11-2017)
Volume 24, Issue 161 (11-2017)
Abstract
Background: Surveying the attitude of interns and emergency medical assistants of Hazrat Rasool hospital in Iran about its educational environment.
Methods: The research had cross-sectional descriptive method. The statistical population included all interns as well as all emergency medical assistants of Hazrat-e-Rasool Hospital in the 2017 first semester of whom 37 samples were selected by census. In this study, the Persian version of the DREEM questionnaire was used. Descriptive statistical methods such as frequency, percentage, mean graph and standard deviation and analytical statistics such as t-test and Chi-square were used to analyze the data.
Results: The results of study showed that the overall score of the educational environment in the group of assistants was 138.52 and in interns was 99±1.28, which had a significant difference between the two groups (p= 0.001). According to McAleer and Rough's Practical Guide, the average between 101 and 150 indicates that positive points were more than negative.
Conclusion: Decision makers and educational planners need more effort to improve the status quo.
Sajad Bastin Takhti, Farzad Firouzi Jahantigh,
Volume 26, Issue 8 (11-2019)
Volume 26, Issue 8 (11-2019)
Abstract
Background: Today, the application of artificial intelligence in the field of health systems has been expanded. Machine learning as one of the sub-branches of artificial intelligence has many applications in the field of medical diagnosis. Chronic kidney disease is one of the most common kidney diseases around the world, which facilitation and acceleration in its diagnosis will have a very favorable outcome for its future treatment. The purpose of this study is to provide an intelligent model based on machine learning techniques for diagnosis of kidney diseases.
Methods: The data used in this study was extracted from 400 patients and non-patients in India. These data were pre-processed in the Python environment and cleared from noisy and outlying observations. Then support vector machine, multilayer perceptron, and decision tree were used for data classification. Accuracy, Recall and Precision evaluation metrics were calculated for performance evaluation of these classifiers.
Results: For the support vector machine algorithm, the Accuracy, Recall and Precision metrics were calculated to be 0.97, 0.961, and 0.986, respectively. The findings indicated that the support vector machine algorithm performs better in terms of Accuracy. In terms of Recall, the decision tree algorithm with the value of 0.963 had the best performance, and in terms of Precision, multi-layer perceptron algorithm with 0.994 had the best performance in data classification.
Conclusion: The results showed that machine learning techniques could be effective in the diagnosis of kidney disease. The use of these techniques can facilitate and expedite the diagnosis and treatment of these patients and increase the likelihood of recovery. The results also showed that the model presented on the basis of machine learning techniques is more accurate, simpler and less expensive than other techniques.
Fateme Maarefi, Sara Zilaee, Roskanak Zaman,
Volume 27, Issue 2 (4-2020)
Volume 27, Issue 2 (4-2020)
Abstract
Background: The purpose was to investigate the effect of organizational learning culture on job satisfaction and relationship quality with customer with mediator variable of organizational agility in Governmental hospitals in Ahwaz city.
Methods: The statistical population included all of staffs of governmental hospitals in Ahvaz city. A total of 370 people were chosen. The methodology of this research was survey administrated through a standard questionnaire. To study hypotheses, the Structural Equation Modeling Modeling and PLS software was used.
Results: All hypotheses were confirmed in this study. The effect of organizational learning culture on job satisfaction, customer relationship quality and organizational agility were 0.512, 0.506 and 0.681, respectively. Also, the effect of organizational agility on job satisfaction and customer relationship quality were 0.439 and 0.484, respectively. The effect of the mediator variable was also confirmed.
Conclusion: According to the results of the study, it is necessary to increase the organizational learning culture as well as organizational agility and remove barriers in order to increase employees' job satisfaction and improve the quality of their relationship with clients.
Shiva Amiri, Majid Jafari-Sabet, Mahmood Hoormand,
Volume 28, Issue 9 (12-2021)
Volume 28, Issue 9 (12-2021)
Abstract
Curcumin is an active yellow substance extracted from the rhizome of Curcuma longa (turmeric). In recent years, curcumin has been reported to have anti-cancer, liver protection, thrombus inhibitor, cardiovascular and anti-arthritis effects. Curcumin has been also known to modulate intracellular signaling pathways that control cell growth, inflammation, and apoptosis (programmed cell death). Curcumin is a powerful antioxidant that reduces circulating free radicals and due to its antioxidant and anti-inflammatory activities can be a potential candidate for the prevention or treatment of neurological diseases and memory disorders. The results of studies in rodents (without induction of memory impairment) have shown that certain doses of curcumin improve memory function. It has been reported that curcumin can prevent and improve age-related memory impairment as one of the causes of irreversible memory impairment over time. However, the results of some studies have shown that in the absence of memory impairment, the effect of curcumin is similar to placebo and does not increase learning and memory. Since the identification of basic and potential mechanisms involved in memory enhancement for therapeutic use of this composition is very important, this article has been reviewed the role of curcumin in improving memory disorders as well as the involved molecular mechanisms. A summary of these mechanisms are outlined below:
•• Modifying monoamines (serotonin, dopamine, and noradrenaline) levels, monoamine oxidase (MAO) activity, acetylcholine-esterase activity, and glutamate release.
Animal studies have shown that curcumin inhibits low levels of monoamine oxidase A (MAO-A) in the brain, increasing serotonin and noradrenaline levels, and in high doses inhibits dopamine metabolism by inhibiting MAO-B enzyme. Biochemical studies have also shown that curcumin significantly increases serotonin and noradrenaline levels in both the frontal cortex and hippocampus.
Studies have also shown that curcumin is effective in reducing cellular reactive oxygen species due to increasing glutamate levels. On the other hand, in the neuronal terminals of the hippocampus, curcumin can reduce the concentration of calcium in the synaptosomes, so this effect is the same as nimodipine as an L-type calcium channel blocker, and thus prevent the neurotoxicity caused by the influx of calcium into the cell.
Also, curcumin plays a role in modulating muscarinic receptors. Moreover, studies have shown that curcumin treatment significantly increases acetylcholine-transferase and regulates acetylcholine-esterase expression which has a positive effect on cognitive function.
•• Providing neuroprotection and enhances neuronal growth by influencing brain-derived neurotrophic factor (BDNF) levels, cyclic adenosine monophosphate (cAMP) activity, and extracellular signal-regulated kinase (ERK) activity.
Increased BDNF plays an important role in brain growth and synaptic plasticity by inhibiting nerve damage and stimulating neurogenesis and cell survival. BDNF / tyrosine kinase B signaling re-leads to phosphorylation and activation of transcription factors such as CREB, which results in long-term gene expression and synaptic changes. In one study, curcumin administration significantly increased the expression of phosphorylated BDNF in the dentate gyrus. Another study showed that curcumin use in rats increased hippocampal neurogenesis by regulating and activating BDNF and 5-HT1A receptors. By activating BDNF, curcumin protects nerve cells from chronic stress and glutamate over-stimulation. In another study, long-term administration of curcumin showed a steady increase in BDNF levels in the amygdala and increased phosphorylation of ERK (Extracellular signal-regulated kinases). ERK1/2 phosphorylation has been shown to activate a set of protein signaling cascades, which in turn leads to a wide variety of cellular processes such as growth, survival, and neuronal cell formation. Activation of ERK signaling pathways leads to phosphorylation of CREB as a major mediator of cell function, survival, and differentiation. Besides, activation of CREB, in turn, leads to increase BDNF gene expression. Zhang et al. (2012) showed that the level of phosphorylated ERK proteins in the amygdala increased rapidly in curcumin-treated animals.
•• Protecting against oxidative stress through activation of antioxidant enzymes, inhibition of lipid peroxidation, metal ion chelation and increasing inducible nitric oxide synthase activity.
Glutathione peroxidase (GPx) and superoxide dismutase (SOD) are the most important antioxidant enzymes that protect against reactive oxygen species (ROS). Curcumin increases the activity of SOD, GPx, and GR enzymes and inhibits the increase of Malondialdehyde (MDA) in a dose-dependent manner which indicates the antioxidant effects of this substance. Moreover, curcumin can reduce beta-oxidation of fatty acids and lipid peroxidation in brain tissue, and given that the intensification of oxidation of such substances can impair learning and memory, so part of the beneficial effect of high-dose curcumin on learning and memory can be attributed to the inhibition of fatty acid oxidation. Studies have also shown that curcumin has a protective effect against damage caused by metal ions such as iron, lead, and cadmium in rat hippocampal cells by chelating them and, thus reducing their neurotoxicity.
Studies have shown that increasing nitric oxide (NO) and activating the nNOS/NO signaling pathway in the rat hippocampus improves memory while inhibiting it with nNOS-specific inhibitors reduces it. Neurological, chemical, and behavioral evidence suggests that pretreatment with curcumin significantly increases nNOS/NO pathway activity in the peripheral cortex (PFC), amygdala, and hippocampus and thus improves memory in mice. On the other hand, 7-nitroimidazole (7-NI), a specific inhibitor of nNOS, reduces the effect of curcumin on improving long-term memory.
•• Modulating inflammation by influencing cytokine gene expression, nuclear factor-κB (NF-κB) activity, tumor necrosis factor-α (TNF-α) levels.
It has been observed that microglia, as the main immune cells in the CNS, can produce inflammatory cytokines to regulate homeostasis and fight pathogens. Overproduction of inflammatory cytokines can lead to neuritis, nerve damage and death. In addition, neuroinflammation is the first major step in neurodegenerative diseases. Curcumin inhibits and reduces the production of inflammatory factors by inhibiting microglia activation. Curcumin exerts its anti-inflammatory effect by inhibiting NF-ƙB activation. Curcumin also reduces and modulates the enzyme COX-2, various inflammatory cytokines such as TNFα, IL-1, IL-6, IL-8 and interferon γ.
Conclusion: There is a lot of evidence that curcumin can play a role in the treatment of neurological diseases such as Alzheimer's, depression, and Parkinson's. Numerous studies have also shown that curcumin has a significant effect on improving learning and memory in various laboratory models and can play a preventive and therapeutic role in this regard. This may be due to its known antioxidant and anti-inflammatory properties, as well as the modulation of neurotransmitters or the regulation of homeostasis of proteins involved in signaling pathways. Although the use of curcumin seems safe according to the results of laboratory studies and clinical trials, it is necessary to prove the effectiveness and safety of curcumin in its short-term and long-term use in clinical trials to improve learning and memory.
Samira Asadzadeh, Zahra Rezaei,
Volume 29, Issue 1 (3-2022)
Volume 29, Issue 1 (3-2022)
Abstract
Background & Aims: The modern world today allows images to be received and stored digitally. To get better results, it is sometimes necessary to make changes to these images. These changes pursue three main goals: image processing, analysis, and comprehension. For this reason, computer image processing systems have been developed to perform these operations with better speed and accuracy. Four major processes occur in these systems: preprocessing, image quality enhancement, image conversion, and image classification and analysis. In these methods, using mathematics, rules have been created by the computer to simulate human visual elements, and it is an aspect of image analysis that is used for specific purposes. Skin imaging systems provide the ability to process images in high volume and with minimal time and cost, as well as increase the accuracy of diagnosis and classification of diseases. These systems, fatigue, human error and other weaknesses that the diagnostician can suffer. Do not have it (1). The first step in diagnosing skin diseases and analyzing digital images of patients with skin lesions is to take a color photograph of the lesion area. One of the most valid methods for this is the use of a dermoscopic device (2). Dermoscopy, also known as dermatoscopy, is an effective tool for dermatologists involved in early diagnosis. Using dermoscopically evaluated pigmented lesions, abnormal structural features are detected and the border of the lesions is accurately observed (3). Accordingly, benign lesions can be detected without the need for biopsy. Dermoscopy increases the accuracy of the diagnosis and helps GPs to correctly identify people with suspected lesions who need to be referred to a specialist. Dermoscopy is also effective in diagnosing non-pigmented skin lesions and inflammatory dermatoses. In dermoscopy, the skin is examined using a special microscope (4).
Methods: The proposed algorithm of this research can be divided into 7 separate steps (loading data set, data integration: data balancing with data amplification or data augmentation technique, data cleaning: clearing images to remove hair noise, slicing images to separate skin from skin Healthy, data conversion: data preparation, convolution neural network design (CNN) and training of the proposed model for image feature extraction, classification combination and mass learning by majority voting method). Which was implemented in Python language in Google Columbine environment and supervised.
For this study, 25,331 dermoscopic images consisting of skin lesions were included (70% educational images, 15% experimental and 15% validation). In data preprocessing, the data were balanced, then the data cleaning operation was performed to remove hair noise, and the data reduction operation was performed to segment the images by separating the lesion from healthy skin. In the next process By designing the convolution neural network, training data were extracted for feature extraction, and by combining the classifiers, an automated system for diagnosing skin diseases was created and evaluated in dermoscopic images.
Results: In the proposed method of hair noise removal, the quality of images is increased and also the separation of the lesion from healthy skin is optimally designed to accelerate image processing to extract high-level features in the convolutional neural network and increase the accuracy of diagnosis and classification to create An automated diagnostic system is a feature of this study compared to other studies. According to the research results, the use of an automated system for the diagnosis and classification of skin diseases and cancers for health-related care is recommended.
Comclusion: Today, the applications of artificial intelligence are not hidden from anyone. Among these, machine learning as one of the most important branches of this field has a special place in all sciences. Deep learning has proven its worth by using the basics of artificial neural networks in solving many issues in the field of medical image processing such as classification. Experts also based on various experiences of using training methods to conclude that there is no single specific training algorithm that can be successful for all applications and has the highest accuracy. Hence they suggest combined learning. According to the important results, although each of the algorithms had a successful performance individually, but combining several algorithms with each other has led to higher accuracy and less error-making decisions. This study is a step towards helping physicians and specialists in diagnosing skin diseases and benign and malignant skin cancers and can help GPs or other physicians to better manage high-risk lesions. Secondary triage as well as avoid unnecessary treatments and minimize biopsy, which is an invasive and costly procedure. This research helps to provide health-related care, forecasting and treatment, as well as cost savings for both patients and health care providers. Also, in deprived areas and far from the specialist, dermoscopic devices with the help of this algorithm can cause timely treatment and reduce patients' costs and time in the field of diagnosis and, if necessary, referral of patients to the desired specialist. Available as a commercial software package. This software package has the ability to connect to dermoscopic devices. By connecting this software package to dermoscopy, a device is created to quickly diagnose skin diseases and cancers. The greatest value of this dissertation is that it is used as a benchmark for designing future studies and evaluating skin cancer diagnosis techniques in patients who are usually examined by a general practitioner and specialist. The findings of the present study are also consistent with the results of Andre and Pachko (2019) research on the diagnosis of skin cancer based on deep learning and entropy for Perth samples (6).
Elham Ghochdashi, Saeed Meshgini, Somayeh Makouei,
Volume 29, Issue 3 (5-2022)
Volume 29, Issue 3 (5-2022)
Abstract
Background & Aims: Mammography is one of the reliable methods for early detection of breast cancer. However, it is difficult for the radiologist to provide an accurate and uniform assessment of the massive mammograms produced in the extensive screening. Therefore, the presence of an intelligent system that is highly accurate in detecting the location of a cancerous mass will be very necessary and necessary. In this regard, this research, by using mammography images and image processing techniques, has been tried to get an accurate diagnosis of the location of breast cancer. For this purpose, first by using some digital image enhancement techniques, an attempt is made to increase the recognition of cancerous tissues, and then by using classification techniques, the precise separation of cancerous parts from healthy parts of the breast is done. In research, various techniques have been proposed to improve the detection of tumors in mammograms and the accuracy of breast cancer classification. The basic problems in breast mammography in identifying and classifying masses and microcalcifications are caused by various factors. One of these complications is due to the awkward and illogical shape of some clusters of calcifications. The boundaries of each of the microcalcifications in the cluster cannot be well defined, and the radiologist may not be able to make an accurate diagnostic decision about the clinical nature of the microcalcifications in an area, but he can usually identify suspicious areas. In the paper, they presented a CAD system for processing mammographic images. They used the compressed breast thickness parameter for feature selection showed the importance of breast compression and changes in breast composition and then applied it to a variety of mammography image processing tasks. Considering that breast thickness is a key parameter in calculations and is not usually recorded; they showed that breast thickness can be estimated from an image and examined its sensitivity on the estimates. Then they discussed how to simulate X-rays in each examination and also simulate the appearance of anatomical structures inside the breast. In the research, tissue characteristics were used to automatically evaluate breast tissue density in digital mammograms. In this approach, the target area is limited to breast tissue only; so that artifacts, background, and head muscles are removed.Breast cancer is the most common cancer among women and the second cause of cancer-related death in women. Mammography is a simple type of imaging and a tool for early detection of non-palpable breast cancers; however, examining and interpreting a large number of mammogram images is a challenging and time-consuming task, and the possibility of human errors is high. One of the most important deep learning methods is convolutional neural networks. In the article, the digital database for screening mammography from the CBIS version was used to improve data validation.
Methods: In this research, three types of architecture were designed in the two-class mode and one type of architecture was designed in the three-class mode. To design the network, the layers were arranged according to Figure 5, which uses an input layer of size 159 x 145 a two-dimensional convolution layer of size 20 x 8, and a maximum integration layer of size 5 x 2, and two fully connected layers. (The maximum integration layer was used because it uses the maximum amount of neuron clusters of the previous layer and also causes faster convergence, and improves generalization and selection of invariant features). The third designed network architecture is shown in such a way that one input layer three 2D convolutional layers three maximum integration layers and two fully connected layers are used, the size of each layer is shown in Table (3). Layering is equal to one. The training time is 6:37 and the accuracy obtained for the validation data is 92.58% and the test data is 86.5%.
Results: The simulation results for 310 data for the second type of two-class architecture, the training time is 6:06, and the accuracy obtained for the validation data is 84.40% and 72.82% for the test data. Also, the simulation results for 1240 The data for the first type of two-class architecture, the training time is 3:44:54, and the accuracy obtained for the validation data is 51.72% and the test data is 51.69%.
Conclusion: After a series of pre-processing, the number of used images was selected as 310. Then two other types of architecture were designed, and by applying the processed data, the accuracy of the architectures for 310 data was 42.39%, 7and 2.82%, respectively. 79.34% was obtained. The accuracy of the architectures for 1240 data was 51.69%, 65.45%, 72.46%. In the three-class mode, 1318 images in the database were used, and due to the lack of the same size, the images were resized. Then the image mask was applied to the images and given to the designed convolutional neural network, and the data was classified into three classes. According to the pre-processing and operations that have been done, the accuracy of the network has increased (72.39%) and the result has improved. The advantage of the method is the increased accuracy of validation and test data.
Methods: In this research, three types of architecture were designed in the two-class mode and one type of architecture was designed in the three-class mode. To design the network, the layers were arranged according to Figure 5, which uses an input layer of size 159 x 145 a two-dimensional convolution layer of size 20 x 8, and a maximum integration layer of size 5 x 2, and two fully connected layers. (The maximum integration layer was used because it uses the maximum amount of neuron clusters of the previous layer and also causes faster convergence, and improves generalization and selection of invariant features). The third designed network architecture is shown in such a way that one input layer three 2D convolutional layers three maximum integration layers and two fully connected layers are used, the size of each layer is shown in Table (3). Layering is equal to one. The training time is 6:37 and the accuracy obtained for the validation data is 92.58% and the test data is 86.5%.
Results: The simulation results for 310 data for the second type of two-class architecture, the training time is 6:06, and the accuracy obtained for the validation data is 84.40% and 72.82% for the test data. Also, the simulation results for 1240 The data for the first type of two-class architecture, the training time is 3:44:54, and the accuracy obtained for the validation data is 51.72% and the test data is 51.69%.
Conclusion: After a series of pre-processing, the number of used images was selected as 310. Then two other types of architecture were designed, and by applying the processed data, the accuracy of the architectures for 310 data was 42.39%, 7and 2.82%, respectively. 79.34% was obtained. The accuracy of the architectures for 1240 data was 51.69%, 65.45%, 72.46%. In the three-class mode, 1318 images in the database were used, and due to the lack of the same size, the images were resized. Then the image mask was applied to the images and given to the designed convolutional neural network, and the data was classified into three classes. According to the pre-processing and operations that have been done, the accuracy of the network has increased (72.39%) and the result has improved. The advantage of the method is the increased accuracy of validation and test data.
Reza Gerami, Ali Salehi,
Volume 29, Issue 3 (5-2022)
Volume 29, Issue 3 (5-2022)
Abstract
Background & Aims: Artificial intelligence (AI) plays an important role in the development of echocardiography for fetal heart disorders. Artificial intelligence, especially deep learning, has shown significant capabilities in reducing the time required for echocardiographic examinations, increasing diagnostic accuracy, and helping to identify anatomical changes and abnormalities in the fetal heart. In the field of fetal heart and blood vessels, artificial intelligence promises to improve prenatal diagnosis of congenital heart disease. This offers the potential to improve screening processes that lead to early diagnosis and intervention in cases of fetal heart disorders. Smart diagnosis based on echocardiography, along with artificial intelligence techniques such as heart segmentation and identification of standard heart parts, helps in more effective and accurate diagnosis. The integration of artificial intelligence in the perinatal diagnosis of congenital heart disease shows its application in improving diagnostic accuracy with continuous efforts in research to further increase its effectiveness. Prenatal diagnosis of congenital heart disease (CHD) in medicine seems to be a solved problem, although challenges continue. Factors affecting fetal congenital heart diseases (CHDs) are diverse and available data in this field are limited. A study of fetal circulatory physiology and brain development in individuals with fetal congenital heart disease provides valuable insights. Advances in prenatal management and intervention for congenital heart disease are the subject of ongoing research that discusses current knowledge, implications, and challenges. Additionally, ongoing investigations such as blood tests to detect dangerous fetal heart defects before birth show promising advances in diagnostic techniques. Over the past decade, there have been significant advances in the prenatal diagnosis of congenital heart defects. While the rate of prenatal diagnosis has increased significantly, some malformations with 3 abnormal vessels are challenging to identify prenatally. Advances in prenatal diagnostic techniques, such as fetal echocardiography, have played an important role in increasing the accuracy of assessing structural heart lesions and dysrhythmic mechanisms. The use of fetal echocardiography has contributed to the growing trend of prenatal diagnosis of congenital heart disease and highlights the impact of evolving diagnostic technologies. The majority of defects identified in fetal life are atrial and ventricular septal defects, and advances continue to address challenges in detecting minor defects. An analysis of the types and trends of prenatally diagnosed fetal heart disorders in the last decade provides insights into the prevalence and characteristics of different types of fetal heart disorders. The purpose of this review study is to evaluate how new technologies can improve the ability of echocardiography to diagnose fetal heart defects.
Methods: In order to thoroughly examine the effects of new technologies on the diagnostic capacities of fetal echocardiography, a full narrative review was conducted using a systematic methodology. We conducted an extensive literature search using well-known academic databases such as Web of Science, ScienceDirect, Scopus, Springer, and Google Scholar. The search approach included targeted keywords pertaining to fetal echocardiography, cutting-edge technology, and enhancements in diagnostics. In order to promote inclusion, we conducted a systematic search of national databases such as the Scientific Information Database (SID), NoorMags, Magiran, and the Islamic World Science Citation Database (ISC) to identify relevant works. The search criteria were limited to papers published until January 2023, encompassing both English and Persian language articles.
Results: In the field of fetal echocardiography, machine learning (ML) brings significant improvements through its application in automated measurements. ML algorithms are effective in automating the measurement of cardiac biometrics and provide accurate assessment of fetal heart structures such as heart chambers. This not only increases efficiency but also ensures accuracy and helps sonographers achieve reliable measurements. Beyond biometrics, ML plays an important role in quality control by evaluating fetal telemedicine audio-visual systems (FTAS) through score-based systems. In addition, ML helps assess the learning curves of sonographers and ensures the quality and consistency of fetal echocardiographic examinations. The versatility of ML programs is evident in fully automated fetal lung ultrasound analysis and shows its ability to deal with various aspects of fetal health monitoring. Additionally, ML is important in hemodynamic quantification, with integrated and automated tools that use ML algorithms to quantify clinically relevant parameters such as B-mode-based pressure and pulse-wave Doppler hemodynamics. These advances underscore the transformative impact of ML in increasing the accuracy, efficiency, and comprehensiveness of fetal echocardiography. Computerized examinations in fetal echocardiography have made significant progress through the integration of machine learning. Studies suggest deep learning-based computer systems for automated echocardiographic examination of the fetal heart. These systems use ML algorithms to predict standard fetal heart shapes, views, and sections, providing valuable insights into congenital heart defects. FetalNet, a deep learning model, improves the detection of congenital heart disease using computer-aided segmentation of standard heart views. In addition, artificial intelligence has shown potential in improving prenatal diagnosis of congenital heart disease and contributing to better prenatal care. The use of deep learning for real cardiac object detection demonstrates the powerful capabilities of computer-aided ML methods in fetal echocardiographic analysis. This investigation demonstrates that STIC, functioning as a dynamic 3D imaging method, enables the ongoing capture of volumetric data from the fetal heart, providing accurate and detailed pictures of cardiac structures and arteries. The incorporation of machine learning (ML) in fetal echocardiography improves the precision of biometric measures, since artificial intelligence systems are skilled at detecting congenital heart abnormalities using conventional images. Moreover, the application of automated assessments and deep learning displays their potential to carefully examine fetal cardiac systems. This integration of technology enables researchers and medical personnel to do more accurate and thorough assessments of fetal cardiac well-being.
Conclusion: The results clearly demonstrate that using modern technology in fetal echocardiography not only enhances diagnostic processes but also has a crucial impact on enhancing treatment and effectively managing fetal cardiac diseases. The integration of imaging technology and artificial intelligence has significant potential for improving diagnostic standards, therefore raising the overall quality of fetal care. The results emphasize the potential revolutionary influence of these technologies on the domain of fetal echocardiography.
Methods: In order to thoroughly examine the effects of new technologies on the diagnostic capacities of fetal echocardiography, a full narrative review was conducted using a systematic methodology. We conducted an extensive literature search using well-known academic databases such as Web of Science, ScienceDirect, Scopus, Springer, and Google Scholar. The search approach included targeted keywords pertaining to fetal echocardiography, cutting-edge technology, and enhancements in diagnostics. In order to promote inclusion, we conducted a systematic search of national databases such as the Scientific Information Database (SID), NoorMags, Magiran, and the Islamic World Science Citation Database (ISC) to identify relevant works. The search criteria were limited to papers published until January 2023, encompassing both English and Persian language articles.
Results: In the field of fetal echocardiography, machine learning (ML) brings significant improvements through its application in automated measurements. ML algorithms are effective in automating the measurement of cardiac biometrics and provide accurate assessment of fetal heart structures such as heart chambers. This not only increases efficiency but also ensures accuracy and helps sonographers achieve reliable measurements. Beyond biometrics, ML plays an important role in quality control by evaluating fetal telemedicine audio-visual systems (FTAS) through score-based systems. In addition, ML helps assess the learning curves of sonographers and ensures the quality and consistency of fetal echocardiographic examinations. The versatility of ML programs is evident in fully automated fetal lung ultrasound analysis and shows its ability to deal with various aspects of fetal health monitoring. Additionally, ML is important in hemodynamic quantification, with integrated and automated tools that use ML algorithms to quantify clinically relevant parameters such as B-mode-based pressure and pulse-wave Doppler hemodynamics. These advances underscore the transformative impact of ML in increasing the accuracy, efficiency, and comprehensiveness of fetal echocardiography. Computerized examinations in fetal echocardiography have made significant progress through the integration of machine learning. Studies suggest deep learning-based computer systems for automated echocardiographic examination of the fetal heart. These systems use ML algorithms to predict standard fetal heart shapes, views, and sections, providing valuable insights into congenital heart defects. FetalNet, a deep learning model, improves the detection of congenital heart disease using computer-aided segmentation of standard heart views. In addition, artificial intelligence has shown potential in improving prenatal diagnosis of congenital heart disease and contributing to better prenatal care. The use of deep learning for real cardiac object detection demonstrates the powerful capabilities of computer-aided ML methods in fetal echocardiographic analysis. This investigation demonstrates that STIC, functioning as a dynamic 3D imaging method, enables the ongoing capture of volumetric data from the fetal heart, providing accurate and detailed pictures of cardiac structures and arteries. The incorporation of machine learning (ML) in fetal echocardiography improves the precision of biometric measures, since artificial intelligence systems are skilled at detecting congenital heart abnormalities using conventional images. Moreover, the application of automated assessments and deep learning displays their potential to carefully examine fetal cardiac systems. This integration of technology enables researchers and medical personnel to do more accurate and thorough assessments of fetal cardiac well-being.
Conclusion: The results clearly demonstrate that using modern technology in fetal echocardiography not only enhances diagnostic processes but also has a crucial impact on enhancing treatment and effectively managing fetal cardiac diseases. The integration of imaging technology and artificial intelligence has significant potential for improving diagnostic standards, therefore raising the overall quality of fetal care. The results emphasize the potential revolutionary influence of these technologies on the domain of fetal echocardiography.
Seyed Mohammad Reza Vagheh Dashti, Dr. Mohammad Saleh Shokouhi Qare Saadlou, Motahareh Isarizadeh, Hamed Bakhtiari Esfandagheh,
Volume 29, Issue 7 (10-2022)
Volume 29, Issue 7 (10-2022)
Abstract
Background & Aims: Language is the most complex and at the same time the simplest means of human communication. In human life, no communication behavior is as extensive and effective as verbal communication. One of the characteristics that separate human societies from animal societies is the use of language. Language is a very important part of any human culture and is a powerful social tool that we learn from childhood. Therefore, the present research was conducted with the aim of investigating the impact of learning a second language on the problem-solving skills, emotional intelligence, and self-expression of 7-12-year-old students.
Methods: The current research was conducted in a causal-comparative manner and by questionnaire method on 240 students from 7 to 12 years of age in Sirjan city. Sampling was done by the available random method and the subjects were randomly divided into two experimental (second language learning) and control (monolingual) groups. The purpose of this research is practical and developmental, and its method is semi-experimental with pre-test and post-test. The statistical population of this research will consist of students from 7 to 12 years of age in Sirjan city in two control groups and experimental groups. Since the number of 100 subjects is sufficient in such research, due to possible attrition, 120 monolingual (Persian) students aged 7 to 12 years old in Sirjan city were used to sample the two experimental groups, and the sampling was voluntary and Available.
Results: Before the start of the 6-month language training course for the experimental group, both groups answered the questionnaire, and after 6 months of intervention in the experimental group, the questionnaire was answered again by both groups. According to Table 2, it is reported that there is a significant difference at the 5% level in the scores related to the self-expression variable between the two groups of monolingual and bilingual students. Therefore, learning a second language for 7-12-year-old students of Sirjan city has had a positive and meaningful effect on their self-expression. In Table 3, you can see the results of the Wilcoxon test to compare the experimental group (second language learning) in the pre-test with the post-test in the three variables of self-expression, emotional intelligence, and problem-solving skills.
Conclusion: The results showed that learning a second language can have a significant effect on the problem-solving skills, emotional intelligence, and self-expression of 7-12-year-old students in Sirjan city (P<0.05). In short, the purpose of this research was to determine the effect of second language education on problem-solving skills, emotional intelligence, and self-expression in 7-12-year-old students of Sirjan City. For this purpose, the current research was conducted in a causal-comparison format with pre-test and post-test. Subjects in the experimental group were taught a second language (English) for 6 months. Predetermined questionnaires were given to the subjects to measure the research variables. After collecting the questionnaires and extracting the necessary data, teaching the second language (English) to the experimental group began for 6 months. In this study, the control group did not receive any intervention. After 6 months, the questionnaire was again delivered to both experimental and control groups. Then the questionnaires were collected and the information was recorded. The analysis of the findings obtained in the pre-test and post-test phases showed that learning a second language has a significant effect on all three variables of problem-solving skills, emotional intelligence, and self-expression of 7-12-year-old students in Sirjan City. Also, it was observed in the intra-group surveys that in the experimental group, a significant difference was observed in all three variables of problem-solving skills, emotional intelligence, and self-expression, comparing the pre-test with the post-test. The importance of learning a second language can be pointed out that not only does a person learn a new language, but also acquires a different cultural perspective through the new language, which can lead to increased globalization. Self-expression is one of the components of social empowerment that helps people to cope with the difficulties of everyday life and cope with stressful situations. Self-expression helps people to live freely and honestly without anxiety and worry. While protecting the rights of others, and expressing their opinions, needs, and rights. Also, self-expression is associated with cognitive changes in children, which causes specific cognitive interpretations about the social environment, self, others, and interactions with them. These cognitive changes caused by self-expression lead to the emergence of (bold schemas) in children, and by using these cognitive schemas, the child shows bold behaviors in interpersonal relationships. Today, despite the changes in culture and changes in the way of life and modernism, many people lack the necessary and basic abilities to face the problems of life and this has made them weak and vulnerable in facing the problems and issues of daily life. Research has shown many health problems and mental and emotional disorders have psycho-social roots.
Methods: The current research was conducted in a causal-comparative manner and by questionnaire method on 240 students from 7 to 12 years of age in Sirjan city. Sampling was done by the available random method and the subjects were randomly divided into two experimental (second language learning) and control (monolingual) groups. The purpose of this research is practical and developmental, and its method is semi-experimental with pre-test and post-test. The statistical population of this research will consist of students from 7 to 12 years of age in Sirjan city in two control groups and experimental groups. Since the number of 100 subjects is sufficient in such research, due to possible attrition, 120 monolingual (Persian) students aged 7 to 12 years old in Sirjan city were used to sample the two experimental groups, and the sampling was voluntary and Available.
Results: Before the start of the 6-month language training course for the experimental group, both groups answered the questionnaire, and after 6 months of intervention in the experimental group, the questionnaire was answered again by both groups. According to Table 2, it is reported that there is a significant difference at the 5% level in the scores related to the self-expression variable between the two groups of monolingual and bilingual students. Therefore, learning a second language for 7-12-year-old students of Sirjan city has had a positive and meaningful effect on their self-expression. In Table 3, you can see the results of the Wilcoxon test to compare the experimental group (second language learning) in the pre-test with the post-test in the three variables of self-expression, emotional intelligence, and problem-solving skills.
Conclusion: The results showed that learning a second language can have a significant effect on the problem-solving skills, emotional intelligence, and self-expression of 7-12-year-old students in Sirjan city (P<0.05). In short, the purpose of this research was to determine the effect of second language education on problem-solving skills, emotional intelligence, and self-expression in 7-12-year-old students of Sirjan City. For this purpose, the current research was conducted in a causal-comparison format with pre-test and post-test. Subjects in the experimental group were taught a second language (English) for 6 months. Predetermined questionnaires were given to the subjects to measure the research variables. After collecting the questionnaires and extracting the necessary data, teaching the second language (English) to the experimental group began for 6 months. In this study, the control group did not receive any intervention. After 6 months, the questionnaire was again delivered to both experimental and control groups. Then the questionnaires were collected and the information was recorded. The analysis of the findings obtained in the pre-test and post-test phases showed that learning a second language has a significant effect on all three variables of problem-solving skills, emotional intelligence, and self-expression of 7-12-year-old students in Sirjan City. Also, it was observed in the intra-group surveys that in the experimental group, a significant difference was observed in all three variables of problem-solving skills, emotional intelligence, and self-expression, comparing the pre-test with the post-test. The importance of learning a second language can be pointed out that not only does a person learn a new language, but also acquires a different cultural perspective through the new language, which can lead to increased globalization. Self-expression is one of the components of social empowerment that helps people to cope with the difficulties of everyday life and cope with stressful situations. Self-expression helps people to live freely and honestly without anxiety and worry. While protecting the rights of others, and expressing their opinions, needs, and rights. Also, self-expression is associated with cognitive changes in children, which causes specific cognitive interpretations about the social environment, self, others, and interactions with them. These cognitive changes caused by self-expression lead to the emergence of (bold schemas) in children, and by using these cognitive schemas, the child shows bold behaviors in interpersonal relationships. Today, despite the changes in culture and changes in the way of life and modernism, many people lack the necessary and basic abilities to face the problems of life and this has made them weak and vulnerable in facing the problems and issues of daily life. Research has shown many health problems and mental and emotional disorders have psycho-social roots.
Morvarid Sheykhi Alizade, Mohammad Hosseinpour, Leila Bahmaee,
Volume 29, Issue 8 (11-2022)
Volume 29, Issue 8 (11-2022)
Abstract
Background & Aims: Due to the rapid and rapid transformation of human knowledge and information, everything is changing rapidly. Organizations interact with the environment as an open system and need to respond to environmental changes in order to survive. Since human resources are the most important factor and axis of organizations, preparing these resources to face changes and update their knowledge is of particular importance. Medical education with the aim of maintaining and promoting human health is very important in order to train human resources specialized in this field. However, medical education is an endless process because medical sciences are advancing at a high speed and on the other hand, due to the escape of knowledge from human memory, its recall is considered necessary for various medical professionals. Continuing education of the medical team is a key element in increasing the knowledge, skills, quality and effectiveness of the health care system and promoting professional competence. The main purpose of continuing medical education is to maintain the skills and professional development of the medical community so that they can meet the needs of patients and the health system and improve the health system. Continuing medical education is a continuous learning process aimed at updating professional competence. This concept is a comprehensive approach to the continuous development of the profession. On the other hand, according to statistical estimates, the number of detainees has tripled since the beginning of the law. Also, the total number of types of continuing education programs (seminar, conference, congress, short-term vocational, codified, conference, workshop and self-study) has increased from 6 programs in 1369 to 9039 programs in 1391, which clearly indicates increasing growth and progress. It will rise in the coming years. In addition, the wide distribution of learners in the country, which according to Article 1 of the Law on Continuing Education includes all medical graduates, as well as the variety of educational topics, necessitates comprehensive and accurate planning, the management of which requires regular information. It is correct and up to date. On the importance of continuing medical education and learning in medical universities, it can be stated that one of the approaches emphasized by international organizations such as UNESCO in the 21st century is the inseparability of continuous learning from human life, which is a collective rethinking. In sensitive professions such as medicine. Education and development: Education, unlike development, can be measured objectively. Rapid changes in all pillars of organizations have severely limited the function of the training tool. For this reason, the scope of the concept of education has been considered. With the expansion and development of the concept of education, the objectivity and measurability of education faces many problems. Today, with the development of the concept of education, in addition to familiarity with skills and techniques, the way of thinking correctly and the system of thinking and analysis of the individual is also targeted and the word development is used instead of the word education. Roger Cartwright (2004) argues that "development is a process in which learning occurs through experience, where learning outcomes affect not only one's work skills but also one's attitudes". The aim of this study was to provide a model of continuing education at Ahwaz Jundishapur University based on a review of studies conducted in this field.
Methods: The statistical population of this study was 90 employees of Ahvaz University of Medical Sciences that 74 people were selected as a sample using Cochran sampling method. Data collection tools were a standard 100-item questionnaire with 7 components including management of education and technical support, technology and design, pedagogical, institutional, ethical, evaluation and feedback, and combined learning templates. The reliability of the questionnaire was confirmed by Cronbach's alpha of 0.8 and its validity was confirmed by face validity with the opinion of experts. SPSS software was used to analyze the data.
Results: The results showed that the factors of education management and technical support, technology and design, pedagogical, institutional, ethical, evaluation and feedback and combined learning forms have a significant effect on the development of continuing medical education.
Conclusion: In general, it can be acknowledged that each educational content should have quality characteristics that meet the goals and strategies of education and in order to continuously develop medical education, education management and technical support factors, technology and design, pedagogical, institutional, Ethics, evaluation and feedback, and forms of blended learning are considered. There are two main approaches to educational design: a systems perspective, and a constructivist perspective. The systematic view of educational design considers education as a process consisting of input, process, and output. In this approach, which is based on the epistemological views of positivism and the psychological views of behaviorism and epistemology, one of its most important underlying assumptions is the acceptance of the existence of knowledge separately and independently outside the all-encompassing mind. As a result of the most important work done in educational design, a very complete and accurate analysis of the subject of education into its components and the classification of these components based on one of the classifications of educational objectives and then determine How to provide training for each of the objectives. Outputs or learning outcomes are first stated very clearly, followed by methods for teaching-learning activities so that students can achieve the desired goals (outcomes) by doing so. . In the constructivist view, which results from postmodern epistemology, knowledge is the product of the process of constructing meaning in the all-encompassing mind. Constructivism is a discourse in the field of learning and psychology that believes that knowledge by means of The person is made. In other words, it is the individual who, according to his previous experiences and knowledge, interprets the new situation and, as a result, forms an interaction with his new knowledge environment. In the first process, new information is added to the cognitive structure, and in the second case, the cognitive structure is changed in order to absorb the new information. In the continuing education programs, it was tried to observe all the above points in a desirable way. For this purpose, a study guide was developed for each program so that the audience or study it is familiar with the program objectives, matching the subject of the program with their needs and interests, duration of scientific validity of the program, evaluation method, cost, continuing education score and program authors.
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