Literature DB >> 32965018

Artificial intelligence technology for diagnosing COVID-19 cases: a review of substantial issues.

M H Alsharif1, Y H Alsharif, S A Chaudhry, M A Albreem, A Jahid, E Hwang.   

Abstract

Today, the world suffers from the rapid spread of COVID-19, which has claimed thousands of lives. Unfortunately, its treatment is yet to be developed. Nevertheless, this phenomenon can be decelerated by diagnosing and quarantining patients with COVID-19 at early stages, thereby saving numerous lives. In this study, the early diagnosis of this disease through artificial intelligence (AI) technology is explored. AI is a revolutionizing technology that drives new research opportunities in various fields. Although this study does not provide a final solution, it highlights the most promising lines of research on AI technology for the diagnosis of COVID-19. The major contribution of this work is a discussion on the following substantial issues of AI technology for preventing the severe effects of COVID-19: (1) rapid diagnosis and detection, (2) outbreak and prediction of virus spread, and (3) potential treatments. This study profoundly investigates these controversial research topics to achieve a precise, concrete, and concise conclusion. Thus, this study provides significant recommendations on future research directions related to COVID-19.

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Year:  2020        PMID: 32965018     DOI: 10.26355/eurrev_202009_22875

Source DB:  PubMed          Journal:  Eur Rev Med Pharmacol Sci        ISSN: 1128-3602            Impact factor:   3.507


  3 in total

Review 1.  A comprehensive review of imaging findings in COVID-19 - status in early 2021.

Authors:  Ali Afshar-Oromieh; Helmut Prosch; Cornelia Schaefer-Prokop; Karl Peter Bohn; Ian Alberts; Clemens Mingels; Majda Thurnher; Paul Cumming; Kuangyu Shi; Alan Peters; Silvana Geleff; Xiaoli Lan; Feng Wang; Adrian Huber; Christoph Gräni; Johannes T Heverhagen; Axel Rominger; Matthias Fontanellaz; Heiko Schöder; Andreas Christe; Stavroula Mougiakakou; Lukas Ebner
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-05-01       Impact factor: 9.236

Review 2.  The diagnostic accuracy of Artificial Intelligence-Assisted CT imaging in COVID-19 disease: A systematic review and meta-analysis.

Authors:  Meisam Moezzi; Kiarash Shirbandi; Hassan Kiani Shahvandi; Babak Arjmand; Fakher Rahim
Journal:  Inform Med Unlocked       Date:  2021-05-06

3.  Forecasting daily Covid-19 cases in the world with a hybrid ARIMA and neural network model.

Authors:  Lucas Rabelo de Araújo Morais; Gecynalda Soares da Silva Gomes
Journal:  Appl Soft Comput       Date:  2022-07-15       Impact factor: 8.263

  3 in total

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