Telehealth Systems for Midwifery Care Management during the COVID-19 Pandemic: A Systematic Review

Background: Intrinsic features of COVID-19 disease, including the severity of the virus transmission and mortality rates, make it difficult to provide obstetric care to pregnant women. In this regard, telemedicine can provide comprehensive midwifery care relying on new technologies, such as virtual clinic, telehealth, tele-monitoring, m-Health, wearable sensors, and the internet of medical things. The objective of this study is to identify the application and requirements of a telehealth system for midwifery care. Methods: We conducted a literature search from 2019/12/1 to 2022/10/1 using the following electronic scientific databases: Web of Science, Scopus, PubMed, Science Direct, and Google Scholar. We carried out hand searches from the reference lists of retrieved studies of journals. Results: We showed that, during COVID-19 pandemic, prenatal care via telehealth increased and telehealth is a good strategy for prenatal and post-partum disease managements. Mental health services are also feasible via telehealth. These new technologies also reduce the risks associated with interpersonal contacts in COVID-19 pandemic. Conclusion: With the COVID-19 pandemic, telehealth became the norm. The future of medical services will be built around this technology and that is a great opportunity to move toward a great evolution. Copyright © 2023, Journal of Iranian Medical Council. All rights reserved. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Identifying Techniques and Models for COVID-19 Prediction

Background: Technologies can predict various aspects of COVID-19, such as early prediction of cases and those at higher risks of severe disease. Predictions will yield numerous benefits and can result in a lower number of cases and deaths. Herein, we aimed to review the published models and techniques that predict various COVID-19 outcomes and identify their role in the management of the COVID-19. Methods: This study was a review identifying the prediction models and techniques for management of the COVID-19. Web of Science, Scopus, and PubMed were searched from December 2019 until September 4th, 2021. In addition, Google Scholar was also searched. Results: We have reviewed 59 studies. The authors reviewed prediction techniques in COVID-19 disease management. Studies in these articles have shown that in the section medical setting, most of the subjects were inpatients. In the purpose of the prediction section, mortality was also the most item. In the type of data/predict section, basic patient information, demographic, and laboratory values were the most cases. Also, in the type of technique section, logistic regression was the most item used. Training, internal and external validation, and cross-validation were among the issues raised in the type of validation section. Conclusion: Artificial intelligence and machine learning methods were found to be useful in disease control and prevention. They accelerate the process of diagnosis and move toward great progress in emergency circumstances like the COVID-19 pandemic. Copyright © 2023, Journal of Iranian Medical Council. All rights reserved. This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Artificial Intelligence in COVID-19 Management: A Systematic Review

With the development of modern technologies in the field of healthcare, the use of Artificial Intelligence (AI) in disease management is increasing. AI methods may assist healthcare providers in the COVID-19 era. The current study aimed to observe the efficacy and importance of AI for managing the COVID-19 pandemic. An organized search was conducted, utilizing PubMed, Web of Science, Scopus, Embase, and Cochrane up to September 2022. Studies were considered qualified for inclusion if they met the inclusion criterion. We conducted review according to the Preferred Reporting Items for Systematic reviews and Meta Analyses (PRISMA) guidelines. There were 52 documents that met the eligibility criteria to be included in the review. The most common item using AI during the COVID-19 era was predictive models to foretell pneumonia and mortality risks in people with COVID-19 based on medical and experimental parameters. COVID-19 mortality was related to being male and elderly based on the Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) logistic regression analysis of demographics, clinical data, and laboratory tests of hospitalized COVID-19 patients. AI can predict, diagnose and model COVID-19 by using techniques such as support vector machines, decision trees, and neural networks. It is suggested that future research should deal with the design and development of AI-based tools for the management of chronic diseases such as COVID-19. © 2023 Samaneh Mohammadi, SeyedAhmad SeyedAlinaghi, Mohammad Heydari, Zahra Pashaei, Pegah Mirzapour, Amirali Karimi, Amir Masoud Afsahi, Peyman Mirghaderi, Parsa Mohammadi, Ghazal Arjmand, Yasna Soleimani, Ayein Azarnoush, Hengameh Mojdeganlou, Mohsen Dashti, Hadiseh Azadi Cheshmekabodi, Sanaz Varshochi, Mohammad Mehrtak, Ahmadreza Shamsabadi, Esmaeil Mehraeen, and Daniel Hackett. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.