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Reza Safdari, Farnoosh Larti, Kamyar Fathi Salari, Saman Mohammadpour,
Volume 14, Issue 3 (7-2020)
Volume 14, Issue 3 (7-2020)
Abstract
Background and Aim: Cardiovascular diseases and medication errors are among the leading causes of morbidity and mortality around the world. Electronic prescribing and Medication Administration(ePMA) systems can prevent medication errors to some extent. This study aimed to determine the information requirements of ePMA systems.
Materials and Methods: This descriptive study was conducted in Imam Khomeini Hospital of Tehran and School of Allied Medical Sciences affiliated to Tehran University of Medical Sciences (TUMS) in the summer of 2019 in two phases: literature review and survey-based questionnaire. Information items obtained from reviewing the texts of 100 articles were organized in three questionnaires. In the survey phase, questionnaires were distributed among physicians, nurses, and the experts of health information management(HIM) and medical informatics, using census sampling method. The reliability of the questionnaires was measured using Cronbach's coefficient alpha. Statistical analysis was done using SPSS.
Results: The findings showed that based on specialists’ point of view, patients' demographic information items and unique identifiers gained the highest average, above 4.7. Physicians agreed most with clinical information, including medication history and generic names. From the nurses’ point of view, the information items of the patients’ problems and the procedures performed and the types of drug doses obtained a complete average of 5.
Conclusion: The need for information items varies among different users of ePMA systems, but there may be items that are common for them. Future studies should further investigate financial and pharmaceutical information requirements based on the perspectives of other hospital pharmacy and accounting staff.
Materials and Methods: This descriptive study was conducted in Imam Khomeini Hospital of Tehran and School of Allied Medical Sciences affiliated to Tehran University of Medical Sciences (TUMS) in the summer of 2019 in two phases: literature review and survey-based questionnaire. Information items obtained from reviewing the texts of 100 articles were organized in three questionnaires. In the survey phase, questionnaires were distributed among physicians, nurses, and the experts of health information management(HIM) and medical informatics, using census sampling method. The reliability of the questionnaires was measured using Cronbach's coefficient alpha. Statistical analysis was done using SPSS.
Results: The findings showed that based on specialists’ point of view, patients' demographic information items and unique identifiers gained the highest average, above 4.7. Physicians agreed most with clinical information, including medication history and generic names. From the nurses’ point of view, the information items of the patients’ problems and the procedures performed and the types of drug doses obtained a complete average of 5.
Conclusion: The need for information items varies among different users of ePMA systems, but there may be items that are common for them. Future studies should further investigate financial and pharmaceutical information requirements based on the perspectives of other hospital pharmacy and accounting staff.
Mona Sarhadi, Mohammad Amin Shayegan,
Volume 15, Issue 1 (3-2021)
Volume 15, Issue 1 (3-2021)
Abstract
Background and Aim: For effective treatment of Alzheimer's disease (AD), it is important to accurately diagnosis of AD and its earlier stage, Mild Cognitive Impairment (MCI). One of the most important approaches of early detection of AD is to measure atrophy, which uses various kinds of brain scans, such as MRI. The main objective of the current research was to provide a computerized diagnostic system for early diagnosis of AD, using leraning machine algorithms, to help physicians. The proposed system diagnoses AD by examining the hippocampal atrophy of brain MRI images and increases the accuracy of the diagnosis.
Materials and Methods: In this study, hippocampus was segmented from the other parts of the brain by using active contour and convolutional neural network and then, three groups of “Normal Controls: NC”, AD and MCI were classified by using the SVM classifier.
Results: The proposed method has succeeded in classifying AD against NC with 98.77%, 98.74% and 97.96% in average for accuracy, sensitivity and specificity, respectively. Also in classification of MCI against NC, the mean accuracy, sensitivity and specificity of 96.14%, 96.23% and 88.21% were achieved, respectively. Compared with the nearest rival method, the proposed method showed improvement accuracy and sensitivity of classification AD from NC with 1.64% and 2.81% respectively. Also, in classification of MCI from NC it showed improvement for accuracy with 8.9% and sensitivity with 2.16%, respectively. Improving in results were due to the use of a modified ACM segmentation algorithm, the use of a combination of features extracted from hippocampal images and features already created by the ImageNet network, the removal of inappropriate features from the feature vector, and the use of deep Inception v3 network.
Concolusion: Based on the results, the combination of polygon surrounding the hippocampus features and deep network features can be useful for detection of AD and MCI.
Materials and Methods: In this study, hippocampus was segmented from the other parts of the brain by using active contour and convolutional neural network and then, three groups of “Normal Controls: NC”, AD and MCI were classified by using the SVM classifier.
Results: The proposed method has succeeded in classifying AD against NC with 98.77%, 98.74% and 97.96% in average for accuracy, sensitivity and specificity, respectively. Also in classification of MCI against NC, the mean accuracy, sensitivity and specificity of 96.14%, 96.23% and 88.21% were achieved, respectively. Compared with the nearest rival method, the proposed method showed improvement accuracy and sensitivity of classification AD from NC with 1.64% and 2.81% respectively. Also, in classification of MCI from NC it showed improvement for accuracy with 8.9% and sensitivity with 2.16%, respectively. Improving in results were due to the use of a modified ACM segmentation algorithm, the use of a combination of features extracted from hippocampal images and features already created by the ImageNet network, the removal of inappropriate features from the feature vector, and the use of deep Inception v3 network.
Concolusion: Based on the results, the combination of polygon surrounding the hippocampus features and deep network features can be useful for detection of AD and MCI.
Najibeh Shenavar, Hashem Atapour, Ameneh Shenavar,
Volume 15, Issue 5 (1-2022)
Volume 15, Issue 5 (1-2022)
Abstract
Background and Aim: Infectious Diseases are among the diseases involved in public health and a high percentage of causes of death worldwide are attributed to these diseases. The purpose of this study was to investigate the status of highly cited articles in the field of infectious diseases based on bibliometrics and Altmetrics indicators.
Materials and Methods: This descriptive-analytical research was applied research that has been done using bibliometrics and Altmetrics methods. The research population included 687 highly cited articles indexed on the Web of Science (WOS) database between 2010-2020. Web of Science database and Bookmarklet tool was used for data collection and VOSviewer, Excel, and SPSS software were used for data analysis.
Results: The production process of highly cited articles have had an upward trend. The highest publication rate was in 2020 and the lowest in 2010. The United States published the largest number of articles with 49%, and Lisa Maragakis and Deborah Yokoe were among the most prolific writers. The Lancet Infectious Diseases Magazine and the Center Disease Control Prevention Institute have contributed the most to the publication of articles citing infectious diseases. Vocabulary: COVID 19, epidemiology, disease, mortality, and infection were the most widely used terms in the field of infectious diseases. Mendeley and Twitter were also among the most important social media sites that cited highly cited articles.
Conclusion: The results showed that there is a significant relationship between Altmetrics indices and the number of citations. Also, by identifying the characteristics of highly cited articles in the field of infectious diseases, an attempt has been made to provide a clear view of the top authors, countries, institutions, and journals, and of course, researchers can use the hot and emerging topics identified in this research in future research.
Materials and Methods: This descriptive-analytical research was applied research that has been done using bibliometrics and Altmetrics methods. The research population included 687 highly cited articles indexed on the Web of Science (WOS) database between 2010-2020. Web of Science database and Bookmarklet tool was used for data collection and VOSviewer, Excel, and SPSS software were used for data analysis.
Results: The production process of highly cited articles have had an upward trend. The highest publication rate was in 2020 and the lowest in 2010. The United States published the largest number of articles with 49%, and Lisa Maragakis and Deborah Yokoe were among the most prolific writers. The Lancet Infectious Diseases Magazine and the Center Disease Control Prevention Institute have contributed the most to the publication of articles citing infectious diseases. Vocabulary: COVID 19, epidemiology, disease, mortality, and infection were the most widely used terms in the field of infectious diseases. Mendeley and Twitter were also among the most important social media sites that cited highly cited articles.
Conclusion: The results showed that there is a significant relationship between Altmetrics indices and the number of citations. Also, by identifying the characteristics of highly cited articles in the field of infectious diseases, an attempt has been made to provide a clear view of the top authors, countries, institutions, and journals, and of course, researchers can use the hot and emerging topics identified in this research in future research.
Reza Safdari, Seyyed Farshad Allameh, Ms Fariba Shabani,
Volume 15, Issue 6 (3-2022)
Volume 15, Issue 6 (3-2022)
Abstract
Background and Aim: Many risk factors can cause biliary system diseases. Hence, this category of diseases is amongst the most common ones. Active patient cooperation is very important in disease management, self-care, and clinical outcomes improvement. A mobile phone application has a high potential in supporting the patients’ self-management. Therefore, this study was conducted to recognize and define data elements to develop a self-care application for biliary patients.
Materials and Methods: The current descriptive study was conducted in 2 stages, resource investigation, and data elements’ need assessment. In the first stage, scientific articles available in databases were used for defining required data elements to develop the application for biliary patients, and a checklist of data elements was prepared. In the second stage, a questionnaire was made based on the checklist. Content and face validity were accepted by the research team and the reliability was calculated 87.2%, using the Cronbach’s alpha test. The mentioned questionnaire was given to Gastroenterologists at Imam Khomeini Hospital complex, and the elected data elements were recognized.
Results: In this application, data elements were categorized into seven sections, including demographic and clinical information, data related to the biliary system diseases, postoperative lifestyle information of the biliary patients, reminders, disease management, and informing. Sixty point five percent of the responders gave the highest importance to data elements in the demographic and clinical data section. Data elements related to patients’ education were considered highly important by 54.2% of the responders. Forty three point eight percent gave the highest importance to data elements in interventional applications’ sections, and only 4.2% gave the least importance to this section.
Conclusion: Based on the identified data elements, a self-care application was designed and developed and can be used as a supplement to specialized care for biliary patients.
Materials and Methods: The current descriptive study was conducted in 2 stages, resource investigation, and data elements’ need assessment. In the first stage, scientific articles available in databases were used for defining required data elements to develop the application for biliary patients, and a checklist of data elements was prepared. In the second stage, a questionnaire was made based on the checklist. Content and face validity were accepted by the research team and the reliability was calculated 87.2%, using the Cronbach’s alpha test. The mentioned questionnaire was given to Gastroenterologists at Imam Khomeini Hospital complex, and the elected data elements were recognized.
Results: In this application, data elements were categorized into seven sections, including demographic and clinical information, data related to the biliary system diseases, postoperative lifestyle information of the biliary patients, reminders, disease management, and informing. Sixty point five percent of the responders gave the highest importance to data elements in the demographic and clinical data section. Data elements related to patients’ education were considered highly important by 54.2% of the responders. Forty three point eight percent gave the highest importance to data elements in interventional applications’ sections, and only 4.2% gave the least importance to this section.
Conclusion: Based on the identified data elements, a self-care application was designed and developed and can be used as a supplement to specialized care for biliary patients.
Alireza Monadi Sefidan, Reza Afrisham,
Volume 16, Issue 3 (8-2022)
Volume 16, Issue 3 (8-2022)
Abstract
Background and Aim: Previous studies have shown that viral and host miRNAs play a role in the process of controlling or progressing the disease and can even be considered as therapeutic targets. Accordingly, the present review study was designed to evaluate the role of host miRNAs and Covid-19 virus in the disease process.
Materials and Methods: The current study was a review study that was conducted during 2012-2022. Studies were extracted from PubMed, Google Scholar, Web of Science and Scopus scientific databases. The researchers selected relevant resources and a summary of them was presented in this review.
Results: The present review study showed that some host miRNAs such as miR-23b-5p, miR-200c, and miR-125a-5p had an inhibitory effect on ACE2 receptor, while miR-3909, miR-4677, and miR-133a had a stimulatory effect on this receptor. Furthermore, host miR-98-5p had an inhibitory effect on TMPRSS2 gene expression. On the other hand, host miR-146a, miR-21, and miR-142 induced inflammation through MAPK and NF-Ƙβ signaling. While, host miR-124, miR-410, and miR-1336 inhibited factor STAT3 and prevented inflammation. Furthermore, host miR-302b and miR-372 targeted the mitochondrial antiviral signaling protein (MAVS), resulting in silencing of type 1 interferon signaling. It has also been established that host exosomal miR-7-5p, miR-24-3p, miR-145-5p, and miR-223-3p inhibited the replication of SARS-CoV-2 and the expression of S protein and their decreased expression in elderly and Diabetic subjects was associated with decreased inhibition of SARS-CoV-2 replication. Moreover, viral miR-359-5p regulated the expression of MYH9 (non-muscle myosin heavy chain 9), which caused virus invasion and release in the host cell.
Conclusion: This study showed that many miRNAs play a role in controlling or progressing the disease of Covid-19 and it is possible to treat the disease of Covid-19 by changing the expression of viral and host miRNA. However, more research is needed in this regard.
Materials and Methods: The current study was a review study that was conducted during 2012-2022. Studies were extracted from PubMed, Google Scholar, Web of Science and Scopus scientific databases. The researchers selected relevant resources and a summary of them was presented in this review.
Results: The present review study showed that some host miRNAs such as miR-23b-5p, miR-200c, and miR-125a-5p had an inhibitory effect on ACE2 receptor, while miR-3909, miR-4677, and miR-133a had a stimulatory effect on this receptor. Furthermore, host miR-98-5p had an inhibitory effect on TMPRSS2 gene expression. On the other hand, host miR-146a, miR-21, and miR-142 induced inflammation through MAPK and NF-Ƙβ signaling. While, host miR-124, miR-410, and miR-1336 inhibited factor STAT3 and prevented inflammation. Furthermore, host miR-302b and miR-372 targeted the mitochondrial antiviral signaling protein (MAVS), resulting in silencing of type 1 interferon signaling. It has also been established that host exosomal miR-7-5p, miR-24-3p, miR-145-5p, and miR-223-3p inhibited the replication of SARS-CoV-2 and the expression of S protein and their decreased expression in elderly and Diabetic subjects was associated with decreased inhibition of SARS-CoV-2 replication. Moreover, viral miR-359-5p regulated the expression of MYH9 (non-muscle myosin heavy chain 9), which caused virus invasion and release in the host cell.
Conclusion: This study showed that many miRNAs play a role in controlling or progressing the disease of Covid-19 and it is possible to treat the disease of Covid-19 by changing the expression of viral and host miRNA. However, more research is needed in this regard.
Marjan Ghazi Saeedi, Mohammad Amin Abbasi Eslamloo, Kobra Darabiyan, Elham Ataei,
Volume 17, Issue 3 (8-2023)
Volume 17, Issue 3 (8-2023)
Abstract
Background and Aim: Despite the continuous progress in medicine, COPD (Chronic Obstructive Pulmonary Disease) is still a progressive, incurable and chronic respiratory condition that limits the patients’ functions in various dimensions, and significantly reduce their quality of life. In this way, self-care of patients and the use of related tools have a significant effect in disease control and treatment. The purpose of this research was design and development of an android-based application for COPD.
Materials and Methods: This research was a descriptive developmental type with a practical approach. Initially, based on the study of library resources, guidelines, and the examination of the medical records of COPD inpatients in Firouzabadi Hospital, a questionnaire was designed to identify the information requirements, data items and features of the application. Then it was reviewed and finalized by a sample of 10 (randomized and convenience sampling) internal and lung specialist doctors in Firouzabadi and Hazrat Rasool hospitals affiliated to Iran University of Medical Sciences. The data was analyzed using descriptive statistics, and then scenario tables and UML diagrams were illustrated to show the overall flow of the application. The application was designed and developed using the Java programming language in the Android Studio 2021 platform. After installing the application on the mobile phones of ten COPD patients of the internal and pulmonary clinic of Firouzabadi Shahre Rey Hospital, and using it for a week, the opinions of the patients about the usability of the application were collected through the QUIS questionnaire, and analyzed.
Results: Application sections were determined following experts’ survey, personal information items, clinical information, disease management, reporting, and training points, which were provided to patients after design for use and evaluation. At the end of the research, the results of the evaluation of the usability and satisfaction level of the application showed that from the patients’ point of view, the application is at a good level with an average score of 1.8 (out of 10 points).
Conclusion: The developed self-care application can be used to increase awareness, help to manage the disease, increase the level of quality of life, and reduce the complications and disease burden for patients with COPD.
Materials and Methods: This research was a descriptive developmental type with a practical approach. Initially, based on the study of library resources, guidelines, and the examination of the medical records of COPD inpatients in Firouzabadi Hospital, a questionnaire was designed to identify the information requirements, data items and features of the application. Then it was reviewed and finalized by a sample of 10 (randomized and convenience sampling) internal and lung specialist doctors in Firouzabadi and Hazrat Rasool hospitals affiliated to Iran University of Medical Sciences. The data was analyzed using descriptive statistics, and then scenario tables and UML diagrams were illustrated to show the overall flow of the application. The application was designed and developed using the Java programming language in the Android Studio 2021 platform. After installing the application on the mobile phones of ten COPD patients of the internal and pulmonary clinic of Firouzabadi Shahre Rey Hospital, and using it for a week, the opinions of the patients about the usability of the application were collected through the QUIS questionnaire, and analyzed.
Results: Application sections were determined following experts’ survey, personal information items, clinical information, disease management, reporting, and training points, which were provided to patients after design for use and evaluation. At the end of the research, the results of the evaluation of the usability and satisfaction level of the application showed that from the patients’ point of view, the application is at a good level with an average score of 1.8 (out of 10 points).
Conclusion: The developed self-care application can be used to increase awareness, help to manage the disease, increase the level of quality of life, and reduce the complications and disease burden for patients with COPD.
Mahboubeh Fatemi, Mohammad Reza Yaghoobi-Ershadi, Yavar Rassi, Mohammad Mehdi Sedaghat, Hassan Vatandoost, Mahboubeh Bayat, Mehrdad Zarabi, Fatemeh Nikpoor, Amir Ahmad Akhavan,
Volume 18, Issue 5 (11-2024)
Volume 18, Issue 5 (11-2024)
Abstract
Background and Aim: Training and proper distribution of human resources are essential for maintaining and promoting society’s health. The first step in any health-related planning is to assess the current situation to draw a clear picture of the future to balance demand and supply. This research was conducted to assess the current situation of medical entomology and vector control/biology and vector control of the diseases in the country and determine the required human resources till 2025.
Materials and Methods: In this study, a “modified and combination model” was used, including the Hall model, which is location-based, and the Australian health workforce estimation model, which is a needs-based approach. The research was carried out in multiple stages, which included collecting the required data (number of faculty members, students, and graduates), the number of private companies providing spraying, and pest control services in all cities of the country and finally holding meetings with the board members of Biology and vector control of diseases and experts to estimate the human resources required in this field.
Results: Based on the data collected and face-to-face interviews conducted during six sessions with the experts in this field, it has been estimated that a total of 985 graduates have been trained in this discipline to date, while there is a requirement for 1,338 graduates in this area. Consequently, it is essential to train an additional 353 individuals in this field by the year 1404. In light of the country’s sixth five-year development plan, which allocates 30 percent of the total student population to postgraduate studies, it is imperative to prepare 35 individuals at the doctoral level, 71 at the master’s level, and 247 at the undergraduate level.
Conclusion: It seems that by accepting an average of 9 people at the Ph.D. level, 18 people at the master’s level, and 62 people at the bachelor’s level, in addition to matching the amount of demand and supply, there will be no problem for the employment of the graduates of this field.
Materials and Methods: In this study, a “modified and combination model” was used, including the Hall model, which is location-based, and the Australian health workforce estimation model, which is a needs-based approach. The research was carried out in multiple stages, which included collecting the required data (number of faculty members, students, and graduates), the number of private companies providing spraying, and pest control services in all cities of the country and finally holding meetings with the board members of Biology and vector control of diseases and experts to estimate the human resources required in this field.
Results: Based on the data collected and face-to-face interviews conducted during six sessions with the experts in this field, it has been estimated that a total of 985 graduates have been trained in this discipline to date, while there is a requirement for 1,338 graduates in this area. Consequently, it is essential to train an additional 353 individuals in this field by the year 1404. In light of the country’s sixth five-year development plan, which allocates 30 percent of the total student population to postgraduate studies, it is imperative to prepare 35 individuals at the doctoral level, 71 at the master’s level, and 247 at the undergraduate level.
Conclusion: It seems that by accepting an average of 9 people at the Ph.D. level, 18 people at the master’s level, and 62 people at the bachelor’s level, in addition to matching the amount of demand and supply, there will be no problem for the employment of the graduates of this field.
Nabeel Taher Jameel Alghanim, Hamed Jadooa Abbas, Hamid Choobineh, Ziba Majidi, Nasrin Dashti,
Volume 19, Issue 2 (7-2025)
Volume 19, Issue 2 (7-2025)
Abstract
Background and Aim: This study investigated the biochemical profiles of individuals with different stages of kidney disease, including those with kidney disease without hemodialysis, chronic kidney disease without hemodialysis, and individuals with renal failure undergoing hemodialysis treatment, to clarify the role of mineral markers, inflammation, and kidney function in the complications of this disease.
Materials and Methods: This case-control study was conducted with 180 participants aged 18 to 81 years in Iraq. Participants were divided into four groups: the case group (including individuals with kidney disease not on dialysis, chronic kidney disease not on dialysis, and kidney failure treated with dialysis) and the control group, which included healthy individuals. Blood levels of urea, creatinine, calcium, phosphorus, vitamin D3, parathyroid hormone (PTH), high-sensitivity C-reactive protein (hs-CRP), and cystatin C were measured.
Results: The results showed that the levels of blood urea, calcium, vitamin D3, cystatin C and hs-CRP were significantly different between the different groups. The mean creatinine in the non-dialysis kidney disease group (3.98±1.77 mg/dL) and non-dialysis chronic kidney disease (4.59±1.63 mg/dL) was different from the dialysis kidney failure group (11.03±3.35 mg/dL) (P=0.001), but there was no significant difference between the two groups of kidney disease without dialysis and chronic kidney disease without dialysis. The phosphorus concentration was significant in all groups (P=0.001) and the highest value was observed in the dialysis kidney failure group. The PTH level was not significantly different between the two groups of non-dialysis, but there was a significant difference compared to the dialysis kidney failure group (P=0.001). Cystatin C was not significantly different in the two non-dialysis groups, but was significantly higher (P=0.001) compared with the renal failure group on dialysis (7.06±1.61 mg/dL).
Conclusion: This study demonstrated that regular monitoring of biochemical biomarkers is essential for the timely diagnosis and effective management of kidney disease. It also highlights the importance of paying attention to metabolic and inflammatory abnormalities in patients with kidney disease (especially in patients on dialysis), including extensive changes in biochemical, hormonal, and inflammatory factors levels that often occur due to severe impairment of kidney function and the dialysis process.
Materials and Methods: This case-control study was conducted with 180 participants aged 18 to 81 years in Iraq. Participants were divided into four groups: the case group (including individuals with kidney disease not on dialysis, chronic kidney disease not on dialysis, and kidney failure treated with dialysis) and the control group, which included healthy individuals. Blood levels of urea, creatinine, calcium, phosphorus, vitamin D3, parathyroid hormone (PTH), high-sensitivity C-reactive protein (hs-CRP), and cystatin C were measured.
Results: The results showed that the levels of blood urea, calcium, vitamin D3, cystatin C and hs-CRP were significantly different between the different groups. The mean creatinine in the non-dialysis kidney disease group (3.98±1.77 mg/dL) and non-dialysis chronic kidney disease (4.59±1.63 mg/dL) was different from the dialysis kidney failure group (11.03±3.35 mg/dL) (P=0.001), but there was no significant difference between the two groups of kidney disease without dialysis and chronic kidney disease without dialysis. The phosphorus concentration was significant in all groups (P=0.001) and the highest value was observed in the dialysis kidney failure group. The PTH level was not significantly different between the two groups of non-dialysis, but there was a significant difference compared to the dialysis kidney failure group (P=0.001). Cystatin C was not significantly different in the two non-dialysis groups, but was significantly higher (P=0.001) compared with the renal failure group on dialysis (7.06±1.61 mg/dL).
Conclusion: This study demonstrated that regular monitoring of biochemical biomarkers is essential for the timely diagnosis and effective management of kidney disease. It also highlights the importance of paying attention to metabolic and inflammatory abnormalities in patients with kidney disease (especially in patients on dialysis), including extensive changes in biochemical, hormonal, and inflammatory factors levels that often occur due to severe impairment of kidney function and the dialysis process.
Mozhgan Farazmand, Mandana Asgari, Hamid Bouraghi, Taleb Khodaveisi, Ali Mohammadpour, Soheila Saeedi,
Volume 19, Issue 3 (9-2025)
Volume 19, Issue 3 (9-2025)
Abstract
Background and Aim: Cardiovascular diseases have a very high prevalence globally and are recognized as one of the main causes of mortality worldwide. Artificial intelligence, as a novel technology, has garnered attention in recent years in Iran and other parts of the world for the management of a wide variety of diseases. The present study aimed to systematically review research studies conducted in the field of applying artificial intelligence in cardiovascular diseases.
Materials and Methods: To investigate research studies conducted in the field of cardiovascular diseases utilizing artificial intelligence, the Persian language databases SID, Google Scholar, and Magiran were searched. This search was conducted without time limitations on April 3, 2024 and included all research studies that, up to this date, had used various artificial intelligence methods in the field of cardiovascular diseases in the present systematic review.
Results: The results of the search in the aforementioned three databases led to the retrieval of 17,819 research studies, of which 46 research studies met the inclusion and exclusion criteria of the study. These research studies had used artificial intelligence in three areas: prediction, treatment, and diagnosis. Neural networks (n=22), support vector machines (n=20), and decision trees (n=16) were the algorithms that were used more than other techniques. The data sources of the included research studies were mainly patient medical records and the UCI database. Additionally, MATLAB software was used more than other software. The most frequently mentioned limitations in the research studies included not considering all factors, limited access to data, insufficient data, the presence of noise in signals or images, and the presence of outliers, missing values, and non-normality of data.
Conclusion: The systematic review of research studies conducted in the field of cardiovascular diseases utilizing artificial intelligence showed that this technology has been used in a wide range of cardiovascular diseases, and most of the conducted research studies confirmed its effectiveness and successful performance.
Materials and Methods: To investigate research studies conducted in the field of cardiovascular diseases utilizing artificial intelligence, the Persian language databases SID, Google Scholar, and Magiran were searched. This search was conducted without time limitations on April 3, 2024 and included all research studies that, up to this date, had used various artificial intelligence methods in the field of cardiovascular diseases in the present systematic review.
Results: The results of the search in the aforementioned three databases led to the retrieval of 17,819 research studies, of which 46 research studies met the inclusion and exclusion criteria of the study. These research studies had used artificial intelligence in three areas: prediction, treatment, and diagnosis. Neural networks (n=22), support vector machines (n=20), and decision trees (n=16) were the algorithms that were used more than other techniques. The data sources of the included research studies were mainly patient medical records and the UCI database. Additionally, MATLAB software was used more than other software. The most frequently mentioned limitations in the research studies included not considering all factors, limited access to data, insufficient data, the presence of noise in signals or images, and the presence of outliers, missing values, and non-normality of data.
Conclusion: The systematic review of research studies conducted in the field of cardiovascular diseases utilizing artificial intelligence showed that this technology has been used in a wide range of cardiovascular diseases, and most of the conducted research studies confirmed its effectiveness and successful performance.
Reza Safdari, Arash Mansourian, Shahram Tahmasebian, Niloofar Mohammadzadeh, Hamideh Ehtesham,
Volume 19, Issue 6 (3-2026)
Volume 19, Issue 6 (3-2026)
Abstract
Background and Aim: Artificial intelligence-based systems can facilitate data management and interpretation in various dental specialties and can be used as auxiliary tools in diagnosis and education. Case-based reasoning is a promising artificial intelligence method for implementing decision support systems in medical sciences. In the current research, this technique has been used to design an intelligent system for the differential diagnosis of oral diseases.
Materials and Methods: This research is a developmental study and is applied in terms of results. To create a database of cases, patient data was collected by referring to the specialized polyclinic of the Faculty of Dentistry at Tehran University of Medical Sciences and through clinical interviews. The [feature-value] collection was used to display the cases. The weight of the features was determined through a specialized Delphi survey conducted at the national level and as an online study. The determined weights were stored in the case database and used as similarity evaluation parameters. Then, the similarity index was calculated for each case.
Results: The intelligent system designed in this research has been developed based on web technologies. Problem-solving in the case-based reasoning method is done in a cycle and includes four main stages: recovery, reuse, review, and maintenance. The input parameters of the system include clinical indicators, paraclinical indicators, historical data, and management data affecting the diagnosis process. The system provides a prioritized list of differential diagnoses of oral diseases across six main axes as output including Ulcerative, vesicular, and bullous lesions, Red and white lesions of the oral mucosa, Pigmented lesions of the oral mucosa, Benign lesions of the oral cavity, Oral cancer, Salivary gland diseases.
Conclusion: The development of the system utilizing case-based reasoning techniques and clinical data processing has the potential to assist dentists in achieving differential diagnosis across six main areas of oral diseases.
Materials and Methods: This research is a developmental study and is applied in terms of results. To create a database of cases, patient data was collected by referring to the specialized polyclinic of the Faculty of Dentistry at Tehran University of Medical Sciences and through clinical interviews. The [feature-value] collection was used to display the cases. The weight of the features was determined through a specialized Delphi survey conducted at the national level and as an online study. The determined weights were stored in the case database and used as similarity evaluation parameters. Then, the similarity index was calculated for each case.
Results: The intelligent system designed in this research has been developed based on web technologies. Problem-solving in the case-based reasoning method is done in a cycle and includes four main stages: recovery, reuse, review, and maintenance. The input parameters of the system include clinical indicators, paraclinical indicators, historical data, and management data affecting the diagnosis process. The system provides a prioritized list of differential diagnoses of oral diseases across six main axes as output including Ulcerative, vesicular, and bullous lesions, Red and white lesions of the oral mucosa, Pigmented lesions of the oral mucosa, Benign lesions of the oral cavity, Oral cancer, Salivary gland diseases.
Conclusion: The development of the system utilizing case-based reasoning techniques and clinical data processing has the potential to assist dentists in achieving differential diagnosis across six main areas of oral diseases.
Fatemeh Beiranvand, Elham Maserat,
Volume 20, Issue 1 (4-2026)
Volume 20, Issue 1 (4-2026)
Abstract
Background and Aim: The rise in chronic diseases is one of the major challenges facing healthcare systems in the 21st century. Developed countries have turned to health-related quality of life recommender systems to address this issue. This study aims to review and evaluate these systems in the context of chronic diseases.
Materials and Methods: A rapid review method was used in this study. The search was performed using a combination of keywords chronic disease, recommender system, health-related quality of life, and evaluation. The most frequently reviewed systems were related to the endocrine and metabolic diseases group with 15 cases (27.3%), which included diabetes (13 cases) and obesity (2 cases). This was followed by cardiovascular diseases with 9 cases (16.4%), including heart failure (7 cases), coronary artery disease (1 case), and hypertension (1 case), as well as respiratory diseases with 7 cases (12.7%), including chronic lung disease (4 cases), asthma (2 cases), and lung transplantation (1 case). Based on the inclusion criteria, some other chronic diseases including blood diseases and immune mechanism-related disorders with 2 cases (3.6%) including AIDS and hemophilia, cancers with 2 cases (3.6%), digestive diseases with 1 case (1.8%) including hepatitis and urinary tract diseases with 1 (1.8%) were investigated in this study, but they did not have a significant contribution. In order to assess the information needs, a search was conducted in library resources in three databases "Google Scholar", "PubMed" and "Scopus", with the aim of extracting articles of recommender systems in the field of health-related quality of life in chronic diseases, assessment tools and frameworks. The studies included 55 articles published in the period from 2003 to 2022.
Results: The extracted systems in the present studies included 15 general mobile-based applications (27.3%), 16 mobile-based self-management and self-care applications (29.1%), 15 general internet platforms, sites and portals (27.3%), and 9 internet self-management and self-care platforms, sites and portals (16.4%). The studies used 4 methods of usability, effectiveness, satisfaction and technical evaluation. In most studies, usability evaluation was used with the aim of usability in 32 cases (58.2%) and effectiveness with the aim of health-related quality of life (behavior, knowledge and attitude) in 25 cases (45.45%). Ease of use features were used in 18 cases (32.7%), user-friendliness in 14 cases (25.45%), and usefulness in 10 cases (18.18%). In the second phase of the study, 98 assessment tools in the form of interviews and questionnaires from 55 studies were extracted and categorized. Ultimately, 50 usability tools, 26 effectiveness tools, 18 satisfaction tools, and 3 technical tools resulted from this study.
Conclusion: With the growing number of mobile health applications and health-related quality of life systems, choosing the most suitable and effective option for users has become a challenging task. Paying attention to standard and evidence-based components of evaluation of these recommended systems can be effective in improving the quality of life related to patients’ health.
Materials and Methods: A rapid review method was used in this study. The search was performed using a combination of keywords chronic disease, recommender system, health-related quality of life, and evaluation. The most frequently reviewed systems were related to the endocrine and metabolic diseases group with 15 cases (27.3%), which included diabetes (13 cases) and obesity (2 cases). This was followed by cardiovascular diseases with 9 cases (16.4%), including heart failure (7 cases), coronary artery disease (1 case), and hypertension (1 case), as well as respiratory diseases with 7 cases (12.7%), including chronic lung disease (4 cases), asthma (2 cases), and lung transplantation (1 case). Based on the inclusion criteria, some other chronic diseases including blood diseases and immune mechanism-related disorders with 2 cases (3.6%) including AIDS and hemophilia, cancers with 2 cases (3.6%), digestive diseases with 1 case (1.8%) including hepatitis and urinary tract diseases with 1 (1.8%) were investigated in this study, but they did not have a significant contribution. In order to assess the information needs, a search was conducted in library resources in three databases "Google Scholar", "PubMed" and "Scopus", with the aim of extracting articles of recommender systems in the field of health-related quality of life in chronic diseases, assessment tools and frameworks. The studies included 55 articles published in the period from 2003 to 2022.
Results: The extracted systems in the present studies included 15 general mobile-based applications (27.3%), 16 mobile-based self-management and self-care applications (29.1%), 15 general internet platforms, sites and portals (27.3%), and 9 internet self-management and self-care platforms, sites and portals (16.4%). The studies used 4 methods of usability, effectiveness, satisfaction and technical evaluation. In most studies, usability evaluation was used with the aim of usability in 32 cases (58.2%) and effectiveness with the aim of health-related quality of life (behavior, knowledge and attitude) in 25 cases (45.45%). Ease of use features were used in 18 cases (32.7%), user-friendliness in 14 cases (25.45%), and usefulness in 10 cases (18.18%). In the second phase of the study, 98 assessment tools in the form of interviews and questionnaires from 55 studies were extracted and categorized. Ultimately, 50 usability tools, 26 effectiveness tools, 18 satisfaction tools, and 3 technical tools resulted from this study.
Conclusion: With the growing number of mobile health applications and health-related quality of life systems, choosing the most suitable and effective option for users has become a challenging task. Paying attention to standard and evidence-based components of evaluation of these recommended systems can be effective in improving the quality of life related to patients’ health.
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