مجله انفورماتیک سلامت و زیست پزشکی
سال هشتم شماره 2 (پیاپی 28، تابستان 1400)

Today, cancer is one of the health concerns in modern societies. Cervical cancer occurs when abnormal cells grow in the cervix. Cervical cancer is one of the fatal cancers in women. Computational models are suitable for simulating complex phenomena such as cancer. Studying cervical cancer through computer modeling and simulation is an effective way to investigate and predict tumor behavior and patients’ response to treatment methods. The objective of this study was to develop a model to simulate the growth and treatment of cervical cancer and study it and analyze the process of chemotherapy in different stages of cervical cancer.

In this research, library study and NetLogo software tool were used for modeling and simulation. First, the anatomy of the uterus, cervix, fallopian tubes, vagina, lymph nodes of the uterus, liver, and lungs was designed and modeled. Then, different stages of cervical cancer were modeled according to TNM system and based on the way the cancer cells spread, tumor growth and spread were simulated and investigated in each stage. The control of tumor by chemotherapy mechanism was also modeled.

The model developed in this study provided the possibility of observing and investigating the growth and spread of tumor in different stages of cervical cancer. Predicting how the cervical tumor grows and behaves is one of the capabilities of the developed model.

This model can be used as an appropriate tool to understand different stages of cervical cancer.

IoT technology offers many benefits in dealing and controlling COVID-19 disease and will greatly facilitate the process of continuous remote patient diagnosis and monitoring with wireless sensors and smart devices. Also, Internet of Things, has positive effects in the field of health and treatment and promotion of electronic health services, including reducing health treatment care costs and delivery of treatment services in a faster manner. This article has been prepared with the aim of investigating the effect of Internet of Things on the promotion of electronic health services for COVID-19 patients from the perspective of treatment staff (physicians and nurses) of Golestan accidents hospital in Kermanshah city.

This research from the type, is quantitative and from the objective, is practical. The statistical sample of the present study consisted of 86 medical staff of Golestan accidents hospital in Kermanshah, which included nurses and physicians. In order to collect data, a researcher-made questionnaire was used, which was approved by 5 experts in the field of health and treatment.

The results indicate that all variables with a positive path coefficient have a positive relationship between the variables and the impact of the Internet of Things on the promotion of e-health services is confirmed and significant.

IoT applications and technology can be a tool for promoting e-health services for COVID-19 patients by educating medical staff and the general public, providing infrastructure, and other appropriate measures, and assisting e-health services in achieving their goals.

COVID-19 has become a pandemic in the world; with the unprecedented growth of technology, smartphones play a role in different stages including managing COVID-19 outbreak. This study aimed to investigate Iranian mobile applications related to Covid-19 disease.

This descriptive cross-sectional study was conducted on all Iranian mobile applications related to COVID-19 in 2020. A search was conducted on Iranian digital app markets on phones with Android and iOS operating systems using the following keywords: ‘COVID-19’, ‘coronavirus’, and ‘corona’ with their Persian translations. For each app, the extracted information included name, category, cost, size, latest version, requirement for Internet connectivity, subscriber count app download, user satisfaction rate, developers, and purpose of development.

The search results indicated a total of 103 apps (76 Android and 27 iOS apps). The most frequent categories were medical apps, games, and educational apps.  About 80.6% of apps were free, 15.5% of apps had in-app purchases, and 3.9% of apps required payment for installation. Most applications were developed by personal developers and 84 applications were online and 19 applications were offline. Other goals of COVID-related application developers in Iran included entertainment, providing daily statistics, raising user awareness, control and tracking, self-care, and prevention.

A wide range of mobile applications related to COVID disease are available with many capabilities. Given the findings and the positive role of this program in epidemic control, the effectiveness of screening and self-assessment programs requires the interaction of private developers and the public sector for integration

Virtual patient simulation (VPS) as a computer interactive simulation has many applications in the field of medical education. This study aimed to investigate medical learners’ views regarding the role of learning based on these types of technologies in surgical training.

In this descriptive-analytical study, the convenience sampling method was used through which 100 interns and residents from Tehran University of Medical Sciences were selected. A researcher-made questionnaire was used to collect data after confirming its validity and reliability. The data were analyzed using descriptive and inferential statistics.

According to the respondents, the greatest impact of VPS can be in creating effective learning and facilitating knowledge acquisition. Among the items examined, the highest mean score was related to the effectiveness of VPS in gaining clinical experience in rare surgeries and lack of available clinical situations (4.27) and the lowest was obtained for the effect of VPS on reducing human error and minimizing it in the patient’s bedside (3.48). VPS-based training has been given priority in cardiovascular and neurological surgeries than in other surgeries. There was also more emphasis on learning rather than evaluation with this technology.

Considering the positive attitude of the respondents towards the role of technologies in improving education, it is recommended to develop and implement these types of educational assistance programs to facilitate the acquisition of basic surgical skills and techniques.

The Integrated Health System SIB is a system designed for electronic health records developed with the objective of information optimization and providing health care. Due to the outbreak of COVID-19 disease and the importance of the electronic health records, the present study aimed to investigate the performance of the shortcomings of SIB and identify the strengths and weaknesses of this system.

The present study was a descriptive cross-sectional one conducted in the Spring of 2020. A total of 166 health workers and health care providers throughout Iran participated in this study. A researcher-made questionnaire with a five-point Likert scale was made available online to individuals. Sampling was done purposively and then the data were entered into SPSS Software (version 26) by the researcher and analyzed using descriptive statistics as well as chi-square test, independent t-test, and correlation coefficient to test a specific hypothesis about the distribution of a variable.

The results of the present study showed that the quality of vaccination registration and its reporting is considered as the best performance of the SIB system. The performance of the entry and exit of the covered people was the weakest part of the SIB system. There was a statistically significant relationship between vaccination registration and gender (p-value = 0.014).

The Findings of this study revealed that the performance of the SIB system in providing health care was evaluated as moderate to good. The results of this study can help system developers to improve the weaknesses of the SIB system in accordance with the existing capabilities.

Coronary artery disease (CAD) is one of the most common causes of death in adults while accurate and early diagnosis can lead to treatment and survival of patients to a great extent. Therefore, the objective of this study was to identify the effective factors leading to this disease and develop a data-driven model to assist physicians in predicting and diagnosing it.

This is an applied research, considering 2038 medical records, collected from Shahid Rajaei Heart Hospital in Tehran, during 5 years. A data preprocessing was carried out and random balanced sampling reduced the dataset into 1000 records, with 500 CAD and 500 Normal. Literature review, consultation with specialist physicians, and weighting using the Chi-square method led to the determination of important features. Support Vector Machine, Neural Network and Random Forest algorithms were applied in RapidMiner and Python.

Among the 35 identified variables, the most important features included VHD, Chest pain, LDL, RWMA, TG, Na, K, BP, and weight. The F-measure, precision, accuracy, and recall for random forest algorithm were calculated as 82.11%, 81.40%, 79.07%, and 85.40%, respectively, and the error rate was 18.6%.

Random Forest predicted the risk of CAD with a reasonable precision. In comparison, due to the large number of input nodes, the error rate of the Neural Network model was relatively higher (23.6%).

Considering the importance of Mobile Health (mHealth) in improving the quality of health services, evaluating the performance of applications that provide these services seems necessary. One of the dimensions of assessing applications is usability evaluation. This study aimed to evaluate the usability of a home health services mobile application (30Teb) using the heuristic evaluation method.

The present study was an applied cross-sectional one. The usability of the user interface of the 30Teb home health services mobile application to provide health services in Kerman was evaluated using a heuristic evaluation checklist. In this study, three evaluators independently evaluated the user interface of the 30Teb mobile app.

A total of 241 problems were identified by three evaluators. After deleting duplicates and then summarizing them, 69 problems remained in the final list. The highest number of identified problems was related to the “identification instead of reminder” and “skills” components, and the lowest number was related to the “privacy” component. Moreover, the highest severity of the identified problems was related to the component of “user freedom and control of the system,” and the lowest severity was related to the “privacy” component.

Although the problems identified in the 30Teb app were not very severe, paying attention to them in different design and implementation stages and solving these problems can lead to better user interaction and more effective use of a mobile application.

Glioma is one of the most common brain tumors, the early and accurate diagnosis of which leads to proper treatment and prolongs the patient’s life. The studies conducted on glioma diagnosis using magnetic resonance imaging images with deep learning methods were reviewed and analyzed in this study.

This study was a systematic review in which PubMed, ScienceDirect, Springer, IEEE, and Arxiv databases were searched between 2010 and 2020 in order to retrieve English language studies using keywords. Then, the articles were selected based on the inclusion and exclusion criteria and in line with the purpose of the research and the required information was extracted for review.

Finally, 35 original research articles were selected. The review of the articles showed that they used a pipeline including collecting images, preprocessing, designing and implementing a model, and evaluating the results of the model for tumor detection, classification, and segmentation. The majority of the articles used public images and pre-trained models. In most articles, Dice similarity coefficient and accuracy criteria were used in segmentation and classification, respectively, as model evaluation criteria.

The results of this study revealed that in most articles, segmentation received more attention in comparison with detection and classification. Therefore, it is suggested that more studies be carried out on detection and especially grading glioma for being utilized in medical diagnostic assistance systems.

Maximizing the efficiency of operating rooms to provide medical services is of great importance. Electronic dashboards are one of the ways to provide quality services in operating rooms by making clinical and managerial information available on time. Therefore, the purpose of this study was to investigate the role of electronic management dashboard in improving the quality of services provided in operating rooms.

In this systematic review study, three databases including PubMed, Web of Science, and Scopus were searched for relevant articles. Selection of articles and data extraction were done by two researchers and the disagreements were resolved in consultation with the third researcher.

From among 116 retrieved articles, 8 studies were selected for the final review. The most important positive consequences of using dashboards were “increasing the efficiency and productivity of the operating room and hospital”, “increasing the satisfaction of surgeons, staff, and patients in performing treatment processes”, and “preventing delays in performing surgeries with the right timing”. Moreover, four challenges related to the use of dashboards in the operating room were identified. Besides, “analyzing surgical processes from the time the patient enters the operating room to his/her exit by providing visual diagrams and text reports” and “determining the time required to perform each operation and setting free time for physicians and operating room staff” were also the most important capabilities of operating room dashboards.

The use of electronic management dashboards can increase the quality of healthmanagement services provided in operating rooms and help managers face less challenges for planning and future foresight