مجله انفورماتیک سلامت و زیست پزشکی
سال دهم شماره 1 (پیاپی 35، بهار 1402)

Pupil and iris disorders form an important category of eye diseases. Accurate segmentation of the pupil is the first and most important step in the automatic diagnosis of diseases related to the pupil and iris. Most of the existing methods do not have enough accuracy and are sensitive to the effects of noise and specular spot reflection. In addition, the images used in these methods usually have limitations, such as the viewing angle.

In the proposed algorithm, a stable method is offered to remove the effects of specular spot reflection in the pupil, and necessary preprocessing is done to detect the exact location of the pupil. An optimized color mapping algorithm is proposed and the mapping is calculated with the help of the LM algorithm to accurately determine the pupil boundary. This method does not impose any restrictions on the eye image and shape, and the angle of the pupil in the image can be in any shape and direction.

The proposed method does not assume any specific model as the final pupil boundary (circle or oval) and is robust to noise and specular reflection factors as well. This method has been able to accurately detect the pupil boundary with the accuracy of 98.8% using UBIRIS dataset and 98% using the collected data by authors.

The method presented in this paper can be used to increase the accuracy in determining the internal and external border of the iris to diagnose diseases related to the pupil and iris, as well as identity identification based on iris tissue.

Stomach cancer is the fourth most common cancer and the second leading cause of cancer death in the world. In addition to significant costs, it imposes many problems on families that is a proper justification for identifying related biomarkers.

The miRBASE website was used to find bioinformatics information. In addition, mirWALK and miRNASN databases were searched to find miRNAs information. To find cancer pathways, the DAVID and KEGG databases were adopted.

The studies conducted on the first pathway reported that microRNA and has-miR-10a-3p, through binding to rs1049216 in the CASP3 gene, by exerting an inhibitory effect on this gene, prevents the development of cancer. In the second pathway, microRNA and has-miR-296-3p, by binding to rs1564483 in the BCL2 gene, by exerting an inhibitory effect on this gene, causes cancer. In the third pathway, microRNA and has-miR-194-3p, through binding to rs7177 in the CCND1 gene, by exerting an inhibitory effect on this gene, prevents the development of cancer.

In this research, cancer pathways involved in cancer were predicted. By finding three SNPs on specific regions of the three above-said genes, we found that these SNPs can be the cause of stomach cancer and can be introduced as biological biomarkers.

Tourism as one of the programs to earn money leads to economic growth; however, due to the non-providing of medical and health services by insurance organizations, the Iranian tourism industry has faced a recession. Therefore, this study aimed to provide a smart tourism health insurance architecture in the context of the Internet of Things (IoT) and cloud to serve tourists.

This practical study was conducted to achieve a smart tourism health insurance architecture. First, its requirements were determined by reviewing the related literature and open interview with four experts from Iran Insurance Corporation. Then, by examining different types of tourism health insurance architectural models, a proposed service-oriented layered architecture was presented on the platform of IoT and cloud. Finally, the proposed architecture was evaluated by the ATAM method.

The proposed architecture has two components (i.e., the requester and the service provider) with user, network, cloud, and database layers that are connected in a service-oriented manner. Also, the evaluation of the proposed architecture by the ATAM method showed that it can respond to the health needs of tourists.

This architecture is presented on the platform of IoT and cloud that provides an intelligent background, and by recording people's data in personal risk profiles, it facilitates providing special tariffs and reducing tourism costs. Therefore, tourists will be able to travel with more confidence in controlling their health status.

COVID-19 has had a devastating impact on public health around the world. Since early diagnosis and timely treatment have an impact on reducing mortality due to infection with COVID-19 and existing diagnostic methods such as RT-PCR test are prone to error, the alternative solution is to use artificial intelligence and image processing techniques. The overall goal is to introduce an intelligent model based on deep learning and convolutional neural network to identify cases of COVID-19 and pneumonia for the purpose of subsequent treatment measures with the help of lung medical images.

The proposed model includes two datasets of radiography and CT-scan. These datasets are pre -processed and the data enhancement process is applied to the images. In the next step, three architectures EfficientNetB4, InceptionV3, and InceptionResNetV2 are used using transfer learning method.

The best result obtained for CT-scan images belongs to the InceptionResNetV2 architecture with an accuracy of 99.366% and for radiology images related to the InceptionV3 architecture with an accuracy of 96.943%. In addition, the results indicate that CT-scan images have more features than radiographic images, and disease diagnosis is performed more accurately on this type of data.

The proposed model based on a convolutional neural network has higher accuracy than other similar models. Also, this method by generating instant results can help in the initial evaluation of patients in medical centers, especially during the peak of epidemics, when medical centers face various challenges, such as lacking specialists and medical staffs.

The internet of Things has become more popular as a new technology since the advent of wireless technology, and attracted attention from supply chain management promoters. This study aimed to identify and analyze IoT applications in the pharmaceutical supply chain agility in the post-COVID-19 era

We first review previous studies in applications of IoT in pharmaceutical supply chain agility and identified the findings through qualitative content analysis. Then, the identified factors were given to 20 management and information technology experts to confirm and validate.

The results showed that IoT applications on the pharmaceutical supply chain agility in 9 components and 50 indicators. First of all, the most important factor is faster communication with other stakeholders (suppliers, manufacturers, distributors, and customers) using the IoT in the supply chain. Secondly, the use of IoT for drug production; manufacturers invest in technologies that lead to lower operating costs in the long term, because network devices and sensors do not make mistakes, do not need rest and training, and do not take vacations. Therefore, they increase the agility of the production process and are a reliable and cost-effective alternative for productivity in production.

The wide applications of the proposed model indicate the need to consider the use of the IoT in the pharmaceutical industry supply chain in order to improve overall supply chain performance and focus on supply chain agility.

One of the important challenges for physicians is the choice of the right antibiotics for various diseases. In this regard, a mobile application could be helpful. The main purpose of this study was to compare the quality of mobile applications designed for an antibiotic prescription to help physicians in choosing the right antibiotic.

In this study, English non-commercial apps from the Apple App Store and the Google Play Store were searched using relevant keywords. The apps were selected and independently scored by an infectious disease specialist and a pharmacist using the Mobile Application Rating Scale (MARS). Kendall’s coefficient of concordance was used to assess inter-rater agreement. The Kolmogorov-Smirnov test was used to verify the normal distribution of the quantitative variables. Spearman's rank-order correlation was applied to determine the relationship between MARS scores and quantitative background variables and Mann-Whitney U tests for dichotomous variables. -

In the initial search until August 2022, 13 apps were eligible for evaluation. The MARS score obtained by applications without in-app advertisements (median: 3.9, IQR: 3.4-6.1) was significantly higher than applications containing advertisements (median: 2.9, IQR: 2.3-8.1) (P=0.029). In the objective domain of MARS, The highest mean domain score belonged to the engagement section (3.9±0.4) and the lowest mean domain score belonged to the functionality section (3.5±0.5).

This study indicated that apps designed to help physicians prescribe antibiotics meet acceptable criteria. Considering objective scores of MARS, lower scores in the “engagement” section demonstrated that designers have paid less attention to this section in comparison to the “information quality” section.

This study aimed to investigate the role of clinical decision support systems in reducing medical errors from the perspective of physicians and nurses in the teaching and therapeutic hospitals.

This descriptive cross-sectional study was conducted in 2021-2022 in two teaching and therapeutic hospitals in Ferdows City, Iran. Physicians and nurses have participated in the research. In this study, the census method was used, and the research community was considered the research sample. The questionnaire of Ariyai et al. was used as a data collection tool. After collecting the questionnaires, the data were analyzed using descriptive statistics methods.

42 medical staff were included in the study. From a physician’s point of view, decision support systems can be helpful by reducing the risk of severe allergic reactions and drug interactions (60%), quick access to patient records (50%), and computerized order registries (30%). From nurses' point of view, eliminating problems related to doctors' handwriting (25%), avoiding  disremember of repeating tests or imaging (18.8%), quick access to updated information during work (12.5%), and reducing the risk of embolism (9.4%) are the advantages of clinical decision support systems.

due to the importance of developing a decision support system in hospitals and measuring the readiness of medical staff to adopt it, it is suggested that the necessary trainings be provided.

During the COVID-19 pandemic, the use of mobile applications increased among people coping with mental health problems. This study aimed to review mobile applications developed or used during the COVID-19 pandemic.

In order to retrieve related articles, the keywords "COVID-19", "Mental health", and "Mobile application" were searched in Web of Science, PubMed, and Scopus databases without a time limit until April 12, 2022. After selecting the articles according to the entry criteria, the features of the introduced applications were checked.

Finally, 25 articles were included in the study, of which 20 applications were developed during the pandemic and five practical programs were already designed. Four applications were designed for medical staff, and 16 were designed for the general public, for each of students, children aged 3-9, COVID-19 patients, cancer patients, and both medical staff and patients with stress disorders one app was designed. The reviewed applications presented educational tips for overcoming negative and destructive thoughts, and getting information about a healthy lifestyle. Also, eight applications provided contractions between users and psychologists. 

The results of this study can be used as a comprehensive source to examine the features and capabilities of mental health applications during the Coronavirus pandemic. It is suggested that by identifying the strengths and weaknesses of such applications, measures should be taken to develop more comprehensive applications that meet users' needs.