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Abstract Background People with polycystic ovary syndrome suffer from many symptoms and are at risk of developing diseases such as hypertension and diabetes in the future. Therefore, the importance of self-care doubles. It is mainly to modify the lifestyle, especially following the principles of healthy eating. The purpose of this study is to review artificial intelligence-based systems for providing management recommendations, especially food recommendations. Materials and methods This study started by searching three databases: PubMed, Scopus, and Web of Science, from inception until 6 June 2023. The result was the retrieval of 15,064 articles. First, we removed duplicate studies. After the title and abstract screening, 119 articles remained. Finally, after reviewing the full text of the articles and considering the inclusion and exclusion criteria, 20 studies were selected for the study. To assess the quality of articles, we used criteria proposed by Malhotra, Wen, and Kitchenham. Out of the total number of included studies, seventeen studies were high quality, while three studies were moderate quality. Results Most studies were conducted in India in 2021. Out of all the studies, diagnostic recommendation systems were the most frequently researched, accounting for 86% of the total. Precision, sensitivity, specificity, and accuracy were more common than other performance metrics. The most significant challenge or limitation encountered in these studies was the small sample size. Conclusion Recommender systems based on artificial intelligence can help in fields such as prediction, diagnosis, and management of polycystic ovary syndrome. Therefore, since there are no nutritional recommendation systems for these patients in Iran, this study can serve as a starting point for such research.

Abstract Background Many early signs of Surgical Site Infection (SSI) developed during the first thirty days after discharge remain inadequately recognized by patients. Hence, it is important to use interactive technologies for patient support in these times. It helps to diminish unnecessary exposure and in-person outpatient visits. Therefore, this study aims to develop a follow-up system for remote monitoring of SSIs in abdominal surgeries. Material and methods This pilot study was carried out in two phases including development and pilot test of the system. First, the main requirements of the system were extracted through a literature review and exploration of the specific needs of abdominal surgery patients in the post-discharge period. Next extracted data was validated according to the agreement level of 30 clinical experts by the Delphi method. After confirming the conceptual model and the primary prototype, the system was designed. In the pilot test phase, the usability of the system was qualitatively and quantitatively evaluated by the participation of patients and clinicians. Results The general architecture of the system consists of a mobile application as a patient portal and a web-based platform for patient remote monitoring and 30-day follow-up by the healthcare provider. Application has a wide range of functionalities including collecting surgery-related documents, and regular assessment of self-reported symptoms via systematic tele-visits based on predetermined indexes and wound images. The risk-based models embedded in the database included a minimum set with 13 rules derived from the incidence, frequency, and severity of SSI-related symptoms. Accordingly, alerts were generated and displayed via notifications and flagged items on clinicians’ dashboards. In the pilot test phase, out of five scheduled tele-visits, 11 (of 13) patients (85%), completed at least two visits. The nurse-centered support was very helpful in the recovery stage. Finally, the result of a pilot usability evaluation showed users’ satisfaction and willingness to use the system. Conclusion Implementing a telemonitoring system is potentially feasible and acceptable. Applying this system as part of routine postoperative care management can provide positive effects and outcomes, especially in the era of coronavirus disease when more willingness to telecare service is considered.

Background: COVID-19 is a global public health problem that is crucially important to be diagnosed in the early stages. This study aimed to investigate the use of artificial intelligence (AI) to process X-ray-oriented images to diagnose COVID-19 disease. Methods: A systematic search was conducted in Medline (through PubMed), Scopus, ISI Web of Science, Cochrane Library, and IEEE Xplore Digital Library to identify relevant studies published until 21 September 2020. Results: We identified 208 papers after duplicate removal and filtered them into 60 citations based on inclusion and exclusion criteria. Direct results sufficiently indicated a noticeable increase in the number of published papers in July-2020. The most widely used datasets were, respectively, GitHub repository, hospital-oriented datasets, and Kaggle repository. The Keras library, Tensorflow, and Python had been also widely employed in articles. X-ray images were applied more in the selected articles. The most considerable value of accuracy, sensitivity, specificity, and Area under the ROC Curve was reported for ResNet18 in reviewed techniques; all the mentioned indicators for this mentioned network were equal to one (100%). Conclusion: This review revealed that the application of AI can accelerate the process of diagnosing COVID-19, and these methods are effective for the identification of COVID-19 cases exploiting Chest X-ray images.

Background and Aim: Diabetes Type II is a chronic metabolic disorder rising its prevalence worldwide. Self-care is the most important management strategy to control the disorder and its adverse effects. The aim of this study was to design and validate an assessment tool to determine the level of self-care of patients affected by Diabetes type II. Materials and Methods: This study was a cross-sectional study. To conduct this study, based on reviewing the texts and reviewing the existing questionnaires, the proposed items were prepared and by eliminating and integrating similar items into a questionnaire in 4 areas related to diet, blood sugar monitoring, Physical activity, drug use was designed with 15 questions. Thirty patients with type 2 diabetes referred to the endocrinology clinic completed a questionnaire. The reliability of the questionnaire was assessed using Cronbach’s alpha and the validity of the questionnaire was assessed by content validity (CVR). Data were analyzed using SPSS software. Results: The results showed that the mean and standard deviation of the age of the studied units was 52.4±12.51 years, of which 50% were female and the other 50% were male. 56% of them had type 2 diabetes for less than 5 years. Also, people with higher education had relatively better metabolic control in diabetes management and patients ‘answer to question 6 had the highest mean, which shows patients’ attention to blood sugar control. To determine the reliability of the questionnaire, Cronbach’s alpha coefficient for all questions was 0.773, with the omission of question 9, it was increased to 0.796. Only two validity questions were 0.66 and 0.16 which were excluded from the test. Pearson correlation coefficient was calculated for each question. Conclusion: The results of this study showed that the questionnaire has the necessary validity and reliability. With this tool, appropriate advice can be provided to patients with type 2 diabetes in the field of self-care, including diet, medication, physical activity and blood sugar control to prevent the progression of the disease and its complications.

BackgroundPatients with colorectal cancer who undergo surgery face many postoperative problems. These problems include the risk of relapse, side effects, and long-term complications. ObjectiveThis study sought to design and develop a remote monitoring system as a technological solution for the postdischarge care of these patients. MethodsThis research was conducted in 3 main steps: system feature extraction, system design, and evaluation. After feature extraction from a systematic review, the necessary features were defined by 18 clinical experts in Iran. In the next step, the architecture of the system was designed based on the requirements; the software and hardware parts of the system were embedded in the architecture, then the software system components were drawn using the unified modeling language diagrams, and the details of software system implementation were identified. Regarding the hardware design, different accessible hardware modules were evaluated, and suitable ones were selected. Finally, the usability of the system was evaluated by demonstrating it over a Skype virtual meeting session and using Nilsen’s usability principles. ResultsA total of 21 mandatory features in 5 main categories, including patient information registration, periodic monitoring of health parameters, education, reminders, and assessments, were defined and validated for the system. The software was developed using an ASP.Net core backend, a Microsoft SQL Server database, and an Ionic frontend alongside the Angular framework, to build an Android app. The user roles of the system included 3 roles: physicians, patients, and the system administrator. The hardware was designed to contain an Esp8266 as the Internet of Things module, an MLX90614 infrared temperature sensor, and the Maxim Integrated MAX30101 sensor for sensing the heartbeat. The hardware was designed in the shape of a wristband device using SolidWorks 2020 and printed using a 3D printer. The firmware of the hardware was developed in Arduino with the capability of firmware over the air. In evaluating the software system from the perspective of usability, the system received an average score of 3.8 out of 5 from 4 evaluators. ConclusionsSensor-based telemonitoring systems for patients with colorectal cancer after surgery are possible solutions that can make the process automatic for patients and caregivers. The apps for remote colorectal patient monitoring could be designed to be useful; however, more research regarding the developed system’s implementation in clinic settings and hospitals is required to understand the probable barriers and limitations.

Background This is a systematic review protocol to identify automated features, applied technologies, and algorithms in the electronic early warning/track and triage system (EW/TTS) developed to predict clinical deterioration (CD). Methodology This study will be conducted using PubMed, Scopus, and Web of Science databases to evaluate the features of EW/TTS in terms of their automated features, technologies, and algorithms. To this end, we will include any English articles reporting an EW/TTS without time limitation. Retrieved records will be independently screened by two authors and relevant data will be extracted from studies and abstracted for further analysis. The included articles will be evaluated independently using the JBI critical appraisal checklist by two researchers. Discussion This study is an effort to address the available automated features in the electronic version of the EW/TTS to shed light on the applied technologies, automated level of systems, and utilized algorithms in order to smooth the road toward the fully automated EW/TTS as one of the potential solutions of prevention CD and its adverse consequences. Trial registration Systematic review registration: PROSPERO CRD42022334988.

Background and Aim: Due to the important role of radiological images for identifying patients with COVID-19, creating a model based on deep learning methods was the main objective of this study. Materials and Methods: 15,153 available chest images of normal, COVID-19, and pneumonia individuals which were in the Kaggle data repository was used as dataset of this research. Data preprocessing including normalizing images, integrating images and labeling into three categories, train, test and validation was performed. By Python language in the fastAI library based on convolution technique (CNN) and four architectures (ResNet, VGG MobileNet, AlexNet), nine models through transitional learning method were trained to recognize patients from healthy persons. Finally, the performance of these models was evaluated with indicators such as accuracy, sensitivity and specificity, and F-Measure. Results: Of the nine generated models, the ResNet101 model has the highest ability to distinguish COVID-19 cases from other cases with 95.29% sensitivity. Other applied models showed more than 96% accuracy in correctly diagnosis of various cases in test phase. Finally, the ResNet101 model was able to demonstrate 98.4% accuracy in distinguishing between healthy and infected cases. Conclusion: The obtained accuracy showed the accurate performance of developed model in detecting COVID-19 cases. Therefore, by implementing an application based on the developed model, physicians can be helped in accurate and early diagnosis of cases. an application based on the developed model, physicians can be helped in accurate and early diagnosis of infected cases.

Background and Aim: Colorectal cancer is one of the most common gastrointestinal cancers among human beings and the most important cause of death in the world. Based on the risk of colorectal cancer for individuals, using an appropriate screening program can help to prevent the disease. Therefore, the purpose of this study was to design a model for screening colorectal cancer based on risk factors to increase the survival rate of the disease on the one hand and to reduce the mortality rate on the other. Materials and Methods: By reviewing articles and patientschr('39') records, 38 risk factors were detected. To determine the most important risk factors clinically, CVR(content validity ratio) was used; and considering the collected data, Spearman correlation coefficient and logistic regression analysis were applied for statistical analyses. Then, four algorithms -- J-48, J-RIP, PART and REP-Tree -- were used for data mining and rule generation. Finally, the most common model was obtained based on comparing the performance of the algorithms. Results: After comparing the performance of algorithms, the J-48 algorithm with an F-Measure of 0.889 was found to be better than the others. Conclusion: The results of evaluating J-48 data mining algorithm performance showed that this algorithm could be considered as the most appropriate model for colorectal cancer risk prediction.

Background and Aim: Stimulants are among the most dangerous substances because of their physical and psychological side effects. The process of treating stimulant abusers is so complicated that makes the treatment very difficult. The aim of the current study is to identify the bits of information needed for the design and development of a mobile application system and a web-based application program to improve the treatment management of stimulant abusers. Materials and Methods: This study is a cross-sectional study in which the community and the sample are matched. All participators work at Iranian National Center for Addiction Studies(INCAS) as stimulant abuse care givers. The data collection tool was a researcher made questionnaire based on the Likert scale, and its validity approved by experts. In addition, the reliability determined by Cronbach alpha method with the score 0/74 analyzed via SPSS. Results: Items that got more than 50% of the participants’ acceptance were identified as the information needed for the system. The most influential items were education and motivational messages with 100% of experts’ agreement. Trauma history and medication therapy, however, got less than 50% and were removed. Other items were put into the study by gaining an average of more than 80% of the experts’ agreement. Conclusion: Emerging technologies in the field of heath can play a significant role in the optimization of stimulant abuse treatment by facilitating the treatment process, increasing the patient participation in the process of treatment, and reducing the costs of patients’ lapse and relapse.

Background: Tuberculosis (TB) is an important infectious disease with high mortality in the world. None of the countries stay safe from TB. Nowadays, different factors such as Co-morbidities, increase TB incidence. World Health Organization (WHO) last report about Iran's TB status shows rising trend of multidrug-resistant tuberculosis (MDR-TB) and HIV/TB. More than 95% illness and death of TB cases are in developing countries. The most infections are in South East Asia and West Pacific that 56% of them are new cases in the world. The incidence is actually new cases of each year. Incidence prediction is affecting TB prevention, management and control. The purpose of this study is designing and creating a system to predict TB incidence by time series artificial neural networks (ANN) in Iran. Methods: This study is a retrospective analytic. 10651 TB cases that registered on Iran’s Stop TB System from March 2014 to March 2016, Were analyzed. Most of reliable data used directly, some of them merged together and create new indicators and two columns used to compute a new indicator. At first, effective variables were evaluating with correlation coefficient tests then extracting by linear regression on SPSS statistical software, version 20 (IBM, Armonk, NY, USA). We used different algorithms and number of neurons in hidden layer and delay in time series neural network. R, MSE (mean squared error) and regression graph were used for compare and select the best network. Incidence prediction neural network were designed on MATLAB® software, version R2014a (Mathworks Inc., Natick, MA, USA). Results: At first, 23 independent variables entered to study. After correlation coefficient and regression, 12 variables with P≤0.01 in Spearman and P≤0.05 in Pearson were selected. We had the best value of R, MSE (mean squared error) and also regression graph in train, validation and tested by Bayesian regularization algorithm with 10 neuron in hidden layer and two delay. Conclusion: This study showed that artificial neural network had acceptable function to extract knowledge from TB raw data; ANN is beneficial to TB incidence prediction.

Background and Aim: Arthritis Rheumatoid is a progressive Chronic disease requiring constant care, and it is important to consult a physician for change in treatment, to see the progress of treatment, and to control the side effects of the drug. One of the information systems used to record information of diseases is the registry or registry system of the disease, which is a database that contains specific information about those who are suffering from a specific illness. Also it is an ideal tool for clinical study. The first step to design a registry is to identify the information needs of the system. Materials and Methods: In this study, 40 randomly selected rheumatologists were entered. The data collection tool was a researcher-made questionnaire including general information, clinical involvement, clinical information, laboratory, imaging, hospitalization and death information; its validity was confirmed by experts and its reliability was estimated by test-retest. Results: After reviewing the average scores, the importance of general information, clinical involvement, clinical information, laboratory, radiology, hospitalization and death information were obtained: 92.9%, 84.8%, 86.2%, 81.2%, 97.04%, 87.2% and 81.2%, respectively. Conclusion: Based on the goals that registries follow, they record different data. Identification of information needs through the involvement of physicians as the experts of this field affects the effectiveness and efficiency of the system. The result of this study is the identification of information needs for designing a registration system for patients with Rheumatoid Arthritis. This information is presented in seven categories.

Introduction: In children under the age of 5 years, unintentional injuries are the main cause of death. Parents play an important role in preventing these injuries. In all around the world, various technology-based interventions have been applied for improving parents’ knowledge and behavior regarding children safety. The aim of this study was to propose a technology-based model for promoting child safety. Method: This applied-descriptive study was conducted in two parts. In the first section, using a questionnaire, 20 physicians were asked to determine the most important information and educational elements. In the second part, the available technologies related to the prevention of unintentional injuries were investigated in terms of their impact on parents' knowledge and attitudes and finally, a technology was selected for the proposed model. Results: The findings in the first part included demographic data, clinical information, training needs including preventive behaviors, making environmental safety and first aid. In the second part, 10 technologies including kiosk, Internet/ web-based programs, computer software and mobile application were found. Mobile applications were the newest applied technologies in this area that had positive effect in change of behavior and knowledge. Therefore, using this technology, a model based on information technology was proposed. Conclusion: Use of information technology, through mobile applications, can provide an opportunity for improving parental knowledge and behavior toward child safety.

Background and Aim: Diabetic foot ulcer is one of the most painful complications of Diabetes and affects various aspects of a personchr('39')s life. Therefore, involvement of the patient in self-care can minimize its complications. The purpose of this study was to identify the effective factors in the Diabetic foot self-management for designing a managed-care based app for people with Diabetes who have risk factors for foot ulcers or wound infections. Materials and Methods: In this cross-sectional study all physicians and nurses employed at ‘Diabetes and Metabolic Diseases Research Center’ of Tehran University of Medical Sciences who had a history of working with Diabetic patients at various levels of Diabetes and foot ulcers participated. The data collection tool was a questionnaire based on the likert scale that made by researchers, which included demographic and clinical information, education and lifestyle management and capabilities of application for self-management in Diabetic foot patients. Finally, descriptive statistics used to analyze the results in SPSS. Results: After reviewing the rate of experts’ agreement with the components of the survey, all the cases over 51% approval rate were considered as required components. According to the opinion of the physicians respectively capabilities of application, education and lifestyle management and patient information and according to the opinion of the nurses’ education and lifestyle management, patient information and capabilities of application were important respectively. Conclusion: Beside proper treatment of the disease, self-management strategies can be effective and useful. Effectiveness of the patients’ self-care by acquiring the necessary skills and involvement in self-care might be achievable.

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Introduction: Gender composition and the soaring trends of drug and tobacco dependency reveal the priority of social skills training related to drug avoidance self-efficacy among female students. The aim of this study was to verify the impact training high school female students to have the social skills needed to avoid the use of drugs. Methods: This study was conducted from September 2012 to May 2013 in two high schools in Ahvaz City in southwest Iran. The participants were divided randomly into two groups of 60 students, one experimental group and one control group using the multi-stage simple sampling method. Two questionnaires, i.e. the ASES and TISS questionnaires, were completed before and after the intervention. Descriptive statistics, chi squared, paired- samples t-test, and the independent-samples t-test were used. Results: The participants had a mean age of 14.93 years. Among the 120 participants, 90.8% indicated that they had never smoked a cigarette, and 51.7% of the participants denied having smoked a hookah. There was no significant relationship between the self-sufficiency means of drug avoidance in the two groups of girls before intervention (p ≥ 0.05). However, after intervention, a significant difference was found in test score of self- efficacy of drug avoidance between the two groups, i.e., 94.91 ± 8.3 for the control group versus 99.16 ± 3.8 for the experimental group, p < 0.05). Significant increases were observed for the pre- and post-test scores of self- efficacy of drug avoidance in the experimental group compared to the control group (99.16 ± 3.8 (p = 0.001) vs. 96.58 ± 6.98 (p > 0.05). The mean values of the pre- and post-test scores of social skill before and after intervention increased significantly only for the experimental group (97.60 ± 19.19 vs. 100.58 ± 12.37, p = 0.03). Conclusion: Educational intervention can significantly enhance social skills for drug avoidance self-efficacy, so it is recommended that such skills be taught in the high school curriculum.

Abstract Tuberculosis, in particular drug-resistant tuberculosis, is of global concern due to the high mortality and morbidity associated with it annually. The aim of this study was to determine the prevalence of and the risk factors for multidrug-resistant tuberculosis in Iran and its neighboring countries. Four databases (PubMed, BioMed Central, EMBASE, and Web of Science) were searched using key terms. Nineteen eligible articles were identified, of which 12 and seven were used for quantitative and qualitative analysis, respectively. The overall pooled estimate of the prevalence of multidrug-resistant tuberculosis, including both new and previously treated tuberculosis cases, in Iran, Iraq, Turkey and Pakistan was 16% (95% confidence interval [CI] 11-20). The patients with a previous tuberculosis treatment history (odds ratio [OR] = 6.45; 95% CI 5.12-7.79), those aged

Background: Pregnancy in women with systemic lupus erythematosus (SLE) is still introduced as a major challenge. Consulting before pregnancy in these patients is essential in order to estimating the risk of undesirable maternal and fetal outcomes by using appropriate information. The purpose of this study was to develop an artificial neural network for prediction of pregnancy outcomes including spontaneous abortion and live birth in SLE. Methods: In a retrospective study, forty-five variables were identified as effective factors for prediction of pregnancy outcomes in systemic lupus erythematosus. Data of 104 pregnancies in women with systemic lupus erythematosus in Shariati Hospital and 45 pregnancies in a private specialized center in Tehran from 1982 to 2014 in August and September, 2014 were collected and analyzed. For feature selection, information of the 149 pregnancies was analyzed with a binary logistic regression model in SPSS software, version 20 (SPSS, Inc., Chicago, IL, USA). These selected variables were used for inputs of neural networks in MATLAB software, version R2013b (MathWorks Inc., Natick, MA, USA). A Multi-Layer Perceptron (MLP) network with scaled conjugate gradient (trainscg) back propagation learning algorithm has been designed and evaluated for this purpose. We used confusion matrix for evaluation. The accuracy, sensitivity and specificity were calculated from the confusion matrix. Results: Twelve features with P<0.05 and four features with P<0.1 were identified by using binary logistic regression as effective features. These sixteen features were used as input variables in artificial neural networks. The accuracy, sensitivity and specificity of the test data for the MLP network were 90.9%, 80.0%, and 94.1% respectively and for the total data were 97.3%, 93.5%, and 99.0% respectively. Conclusion: According to the results, we concluded that feed-forward Multi-Layer Perceptron (MLP) neural network with scaled conjugate gradient (trainscg) back propagation learning algorithm can help physicians to predict the pregnancy outcomes (spontaneous abortion and live birth) among pregnant women with lupus by using identified effective variables. [ABSTRACT FROM AUTHOR]