مجله انفورماتیک سلامت و زیست پزشکی
سال هشتم شماره 1 (پیاپی 27، بهار 1400)

Today, several methods are used for detecting COVID-19 such as disease-related clinical symptoms, and more accurate diagnostic methods like lung CT-scan imaging. This study aimed to achieve an accurate diagnostic method for intelligent and automatic diagnosis of COVID-19 using lung CT-scan image processing techniques and utilize the results of this method as an accurate diagnostic tool complementing the CT-scan devices.

Based on digital image processing algorithms such as segmentation and feature extraction and using various methods of statistical analysis on the features extracted from images, CT-scan images of 79 male and female patients in different ages were analyzed and the effects of this disease on the infected lungs of patients were evaluated. This research was conducted in the spring of 2020 in the Faculty of Medical Sciences and Technologies, Science and Research Branch in Tehran.

This intelligent method based on feature extraction from lung CT-scan images can diagnose COVID-19 with high accuracy on different categories (gender, type of injury caused by the disease). The analysis of lung tissue involvement in patients with COVID-19 revealed that most patients had tissue damage in the lower parts of both lungs to a greater extent than the middle and upper lung lobes.

The algorithm presented in this study can accurately detect and differentiate the data of images taken from the lungs of healthy people and patients with coronavirus disease.

Obesity and hypertension are community health problems. The objective of this study was to diagnose obesity and hypertension in Isfahani students by artificial neural network.

The present study was a diagnostic and predictive one that used the information of 460 students aged 7-18 years old in Isfahan to design a neural network with 11 input variables (age, sex, weight, height, waist circumference, body mass index, waist to height ratio, abdominal obesity, physical activity, genetics, and unhealthy eating behaviors) and three output variables of obesity, systolic blood pressure, and diastolic blood pressure. Conjugate Gradient and Levenberg-Marquardt algorithms were used for network training.

Selected neural network with the Levenberg algorithm has 16 hidden neurons in the diagnosis of obesity and high diastolic blood pressure and 14 hidden neurons in the diagnosis of high systolic blood pressure. The sensitivity, specificity, and accuracy of the network in the diagnosis of obesity were 0.9591, 0.9975, and 0.9934, respectively, and these values were 0.8461, 0.9949, and 0.9739 for high systolic blood pressure and 0.7952, 0.9973, and 0.9609 for high diastolic blood pressure. It was observed that the designed network detects obesity in children and adolescents with a high accuracy of 95% and diagnoses systolic and diastolic blood pressures with a high accuracy of 84% and 79%, respectively.

According to the results, about 83% of obese adolescents have hypertension. Therefore, there it is necessary to design educational programs in the field of behavioral changes, including physical activity along with interventions in nutrition planning for students.

Laryngectomy is the only known treatment for laryngeal cancer. The objective of this study was to design and develop a self-care application for patients undergoing laryngeal resection surgery.

This study was an applied-developmental one conducted in two stages. The first stage included assessing the need for data elements using questionnaire by 14 ear, nose, and throat (ENT) specialists. Then, the application prototype was developed in Java. In the second stage, the usability of the application was evaluated using standard questionnaire of usability evaluation.

The results of the first stage included two main parts of clinical information and application capabilities. The clinical information consisted of 14 main groups and 35 subgroups with a mean score of 9.9. The application capability also included alarm, reminder, messaging, social networks, clinic reservation, and Internet connection. According to the participants, all information items of the questionnaire were declared as necessary. The usability of the application was also evaluated at a good level with an overall average of 7.6.

Using a self-care application for post-laryngectomy patients will help increase their knowledge and facilitate self-care. Thus, it could be expected that using self-care technology and applications would reduce patients’ visits to health care centers as well as the number of their educational and care questions asked from health care staff. However, it is suggested that further studies be conducted with larger population and in different provinces.

Phenylketonuria is one of the most common autosomal recessive metabolic diseases, characterized by a wide range of neuropsychological and neurocognitive disorders. Without proper care, control, and management, this disease can lead to severe mental retardation and neurobehavioral disorders. Therefore, the objective of this study was to design and develop a Minimum Data Set (MDS) for phenylketonuria disease to introduce indicators effective in further management, control, and monitoring of this disease.

The present study was a descriptive-analytical one that was conducted in two stages. In the first stage, a comprehensive review of PubMed, Web of Science, and Scopus databases was performed to identify management and clinical data elements. Then, the necessary data elements were extracted from the studies and put into a questionnaire. In the second stage, 15 pediatricians, nutritionists, psychiatrists, and psychologists completed the designed questionnaire using the two-stage Delphi technique. The descriptive statistics as well as SPSS 23 were used to analyze the data.

A total of 133 management and clinical data elements were extracted from the studies. These data elements were divided into three groups of information and 14 categories. According to experts, consensus and collective agreement were reached on 125 data elements in 13 categories. The category of congenital defects was the only category all data elements of which were excluded in the study.

Given the clinical and economic challenges that phenylketonuria patients face, determining the minimum data set can enable effective control and management of this disease, reduce costs, and improve management of information relevant to these patients.

Ovarian cancer is caused by malignant changes in the cells of the ovary. In various studies, cancer has been regarded as a biological system that has a set of characteristics; therefore, presenting a model of this system can considerably help to study and predict the phenomena related to the growth and spread of cancer. This study attempted to model and simulate the growth and spread of tumor in different stages of ovarian cancer and investigate the process of metastasis of ovarian cancer to other tissues and organs.

In this research, modeling and simulation as well as library studies were used. First, it was attempted to design and model the anatomy of the ovary, fallopian tubes, uterus, lymph nodes of the ovaries, liver, and spleen. Then, different stages of ovarian cancer were modeled and simulated according to TNM system. NetLogo software was used for modeling and simulation.

This study developed a model to simulate, visualize, and predict tumor growth and spread in different stages of ovarian cancer according to the TNM system as well as the ovarian cancer metastasis process.

By providing the possibility of further study and testing, development and utilization of models of ovarian cancer in its different stages can lead to better prediction of tumor growth sequence and behavior and selection of a better treatment option

Today, the use of green information technology has positive effects on the health system, especially in controlling energy waste, reducing paperwork, lowering the use of technology and medical devices, and enhancing environmental and economic performance. This study aimed to investigate the challenges of implementing green information technology in hospitals and treatment centers.

This study was an applied research carried out in two phases: first, by reviewing the theoretical foundations and previous studies through the method of reviewing texts, the challenges of establishing green information technology in hospitals and medical centers were identified; then, to confirm and prioritize the identified challenges, these challenges were provided to 21 academic experts and IT officials working in hospitals.

On the path of implementing green information technology, hospitals and treatment centers faced challenges related to Human Resource Management System (2 components and 10 indicators), Institutional Pressures (4 components and 9 indicators), Administrative System (3 components and 16 indicators), Financial System (4 components and 4 indicators), Legal System (2 components and 3 indicators), and Organizational Infrastructure (2 components and 2 indicators).

The implementation of green information technology in hospitals and treatment centers has a unique potential in reducing energy, promoting the recyclability of obsolete equipment and hospital waste, creating environmentally friendly products, and encouraging people to work remotely.

Most skin cancers are treatable in the early stages; thus, an early and rapid diagnosis can be very important to save patients’ lives. Today, with artificial intelligence, early detection of cancer in the initial stages is possible.

In this descriptive-analytical study, a computerized diagnostic system based on image processing techniques was presented, which is much more helpful for the patient. In this method, dermoscopic images of actinic keratosis and squamous cell carcinoma were improved by preprocessing techniques and the potential noises were removed. Then, segmentation was performed using the thresholding method to separate the lesion from the underlying skin. Thereafter, from the segmented area, texture, shape, and color information and features were extracted. Finally, the feature reduction method and support vector machine (SVM) were used to evaluate the proposed method qualitatively and quantitatively.

The data in this study included 100 samples of actinic keratosis images and 100 samples of squamous cell carcinoma. The results of the present study showed that using the genetic algorithm method together with the support vector machine method could help identify the type of skin cancer with 99.7 ± 0.4% accuracy.

The effect of different tissue features in diagnosing the type of lesion showed an increase in the amount and variety of features extracted from the samples would lead to better training and more accurate analysis of the system.

Due to the prevalence of coronavirus, the role of media, especially social media, to inform about and increase health literacy is very important. The objective of this study was to investigate the influence of factors effective in the use of social media on the level of e-health literacy of citizens in crisis.

In this descriptive-analytical study, 384 patients in Kermanshah province were selected using Morgan table. For this purpose, a two-part researcher-made questionnaire was used which included a demographic section and a specific section (using 5 variables) with 40 questions. The items in this questionnaire were scored using a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).

The findings revealed that 43.1% of the participants believed Corona had a great impact on their job and more than 40% of the participants also stated they obtained health information through the Internet. The results also showed that the relationship between the variables was significant (P <0.05).

The highest correlation was observed between accessibility and popularity variables. This suggests that increasing access to social media will enhance the popularity of these media. The information obtained in this study demonstrated that up-to-dateness of media, accessibility, trust in social media, perceived value, and popularity are among the factors effective in the impact of social media on e-health literacy.

In recent years, due to physical problems of patients or restrictions on social interactions in COVID-19 pandemic, telerehabilitation based on virtual reality (VR) attracted the attention of many researchers.  

In this applied research, a Kinect sensor and an image processing software were used to design and implement a motion simulator for rehabilitation of patients with physical problems in upper or lower limb. To do this, the patient was supposed to touch or catch moving objects such as a bee. The movement of objects was based on physician’s decision and while comparing patients’ movements with reference ones in the designed system, the patient was encouraged to correct his/her movements to finally be able to do them in the desired manner. In the designed system, it is possible to monitor the patient’ movements outside the clinical environment and based on telerehabilitation. 

The efficiency of the designed system in patients with physical disabilities was investigated based on quantitative analysis (via motion charts) and qualitative analysis (through questionnaire).

The results showed the high efficiency of this system in analysis of patients’ movements (through comparing it with reference ones), and the possibility to practically use it in rehabilitation centers especially in recent situation of covid-19 pandemic

P53 is a tumor suppressor protein with numerous missense mutations identified in its gene. These mutations are observed in a vast number of cancers. R213G is one of them which has a role in metastatic lung cancers. In this research, R213G was studied in comparison with the wild type via molecular dynamics simulation.

For the three-dimensional structure of the wild-type P53 protein, chain A was used from crystallographic structure with PDB ID: 1TSR.  For R213G mutation, residue 213 of this structure was changed to glycine. Molecular dynamics simulation was repeated twice for 15 ns using Gromacs 5.1.2 software package, AMBER99SB force field, and TIP3P as water model. RMSD, RMSF, radius of gyration, and potential energy analyses were performed on resulted trajectories.

RMSF analysis showed that the R213G mutation changes the flexibility of 11 residues including R-248. These residues are not near the mutated position, but all of them are located on 220-250 fragment of this domain or are residues in the neighbor of this fragment. The radius of gyration and potential energy results confirmed a reduction in stability of this protein as a result of this mutation.

RMSF analysis of R213G mutation beside the changes in stability and radius indicated that this mutation could greatly affect the P53 interactions with other macromolecules.

Meaningful use program indicates that health care organizations make meaningful use of an accredited electronic health record system. The objective of this study was to investigate the process of “meaningful use” program and the related challenges for the development of electronic health record.

This review study was conducted in 2020. The PubMed, Scopus, Science Direct, and Google scholar databases were searched from 2010 to October 2019 using “Meaningful Use Program” and “Electronic Health Record” as keywords. After applying the inclusion criteria, 20 articles were reviewed.

Based on the studies, the measures related to “Meaningful Use” program included 15 main measures, 5 selected sub-measures, and 6 qualitative clinical measures. The program includes three steps: data recording and sharing, advanced clinical processes, and improving health outcomes. The primary criteria for receiving the incentive amount of “Meaningful Use” program included computerized physician order entry, investigation of drug interactions, creation of electronic prescriptions, and registration of demographic characteristics. The challenges of this program included the time-consuming nature of the program, the lack of control over infrastructure development, unusable technologies of electronic health records, and the all-or-nothing approach to certification.

The use of new programs and solutions like “Meaningful Use” program can lead to the development of electronic health record and enables more secure exchange of information.

Anesthesia is one of the processes in which medication error is common. One of the most frequent medication errors in anesthesia is the error in calculating the dose of drugs. Decision support systems are one of the available solutions to reduce these errors. The purpose of this study was to design and evaluate an anesthesia decision support mobile application to assist anesthesiologists to choose the right and timely prescription of drugs.

This practical cross-sectional study was conducted in three stages including needs assessment, design, and evaluation. The needs assessment of this mobile app was performed using semi-structured interviews with physicians. After drawing the initial design, the mobile app was developed for the Android operating system, and in the end, it was evaluated based on accuracy and speed of calculation indicators.

The output of the interviews was classified into three categories including physicians’ views on the need to develop the app, the expected facilities and capabilities of the app, and the information content of the app. The evaluation results of this app showed that in all groups, except the expert group, the calculation time required by physicians was longer than the one by the mobile app. Moreover, the highest number of correct answers was given by the app user group and then the fourth-year resident group.

An anesthesia decision support mobile app for physicians can significantly increase the accuracy and speed of physicians’ medication calculations. So far, no anesthesia mobile app has been developed in Iran according to valid international guidelines. Therefore, the results of this study can be taken into consideration by health service providers.