BACKGROUND: In order to provide the best possible care for COVID-19 patients and reduce the burden on the health care system, accurate and timely diagnosis and effective prognosis of this disease is important . Machine learning methods can play vital roles in diagnosing COVID-19 by processing chest x-ray images .
OBJECTIVE: Our aim of this study is to summarize information on the use of intelligent models for diagnosing and prognosing the COVID-19 to help early and timely diagnosis of the disease to help with health .
METHODS: A systematic search of the PubMed, Web of Science, IEEE, ProQuest, Scopus, bioRxiv, and medRxiv databases up to 24 May 2020 is performed . To conduct this study, PRISMA guidelines were followed . All original articles applying image processing for predicting and diagnosing the COVID-19 disease were considered . Two reviewers independently assessed original papers to determine eligibility for inclusion . Risk of bias was evaluated by using Prediction Model Risk of Bias Assessment Tool (PROBAST).
RESULTS: Of the 629 articles retrieved , 45studies were included . The review identified 4 prognosis models for calculating prediction of disease severity and estimation of confinement time, for individual patients, and 41 diagnosis models for detecting COVID-19 from normal or other pneumonias . Most articles used deep learning methods based on CNN networks which have been used widely as a classification algorithm The most frequently reported predictors of prognosis in patients with COVID-19 included age, CT data, gender, comorbidities, symptoms and laboratory findings . Deep CNN obtained better results compared with non-Neural Network-based methods . Moreover, all of the models are in high risk of bias due to the lack of information about study population, intended groups and inappropriate reporting .
CONCLUSIONS: Machine learning models for diagnosis and prognosis of COVID-19 showed excellent discriminative performance approximately . However, these models were at high risk of bias, because of various reasons like low information about participants, randomizing process and lack of external validation . Therefore, it leads to optimistic report in their models . In hence this review doesn't recommend any of the current models to be used in practice . CLINICALTRIAL :