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Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence.
Comput Math Methods Med. 2020; 2020:9756518.CM

Abstract

The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms "deep learning", "neural networks", "COVID-19", and "chest CT". At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks.

Authors+Show Affiliations

Department of Biomedical Engineering, Near East University, Nicosia / TRNC, Mersin-10, 99138, Turkey. DESAM Institute, Near East University, Nicosia / TRNC, Mersin-10, 99138, Turkey.DESAM Institute, Near East University, Nicosia / TRNC, Mersin-10, 99138, Turkey. Department of Artificial Intelligence Engineering, Near East University, Nicosia / TRNC, Mersin-10, 99138, Turkey.Department of Biomedical Engineering, Near East University, Nicosia / TRNC, Mersin-10, 99138, Turkey.Department of Biomedical Engineering, Near East University, Nicosia / TRNC, Mersin-10, 99138, Turkey. DESAM Institute, Near East University, Nicosia / TRNC, Mersin-10, 99138, Turkey.Department of Biomedical Engineering, Near East University, Nicosia / TRNC, Mersin-10, 99138, Turkey. DESAM Institute, Near East University, Nicosia / TRNC, Mersin-10, 99138, Turkey.

Pub Type(s)

Journal Article
Review

Language

eng

PubMed ID

33014121

Citation

Ozsahin, Ilker, et al. "Review On Diagnosis of COVID-19 From Chest CT Images Using Artificial Intelligence." Computational and Mathematical Methods in Medicine, vol. 2020, 2020, p. 9756518.
Ozsahin I, Sekeroglu B, Musa MS, et al. Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence. Comput Math Methods Med. 2020;2020:9756518.
Ozsahin, I., Sekeroglu, B., Musa, M. S., Mustapha, M. T., & Uzun Ozsahin, D. (2020). Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence. Computational and Mathematical Methods in Medicine, 2020, 9756518. https://doi.org/10.1155/2020/9756518
Ozsahin I, et al. Review On Diagnosis of COVID-19 From Chest CT Images Using Artificial Intelligence. Comput Math Methods Med. 2020;2020:9756518. PubMed PMID: 33014121.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence. AU - Ozsahin,Ilker, AU - Sekeroglu,Boran, AU - Musa,Musa Sani, AU - Mustapha,Mubarak Taiwo, AU - Uzun Ozsahin,Dilber, Y1 - 2020/09/26/ PY - 2020/06/26/received PY - 2020/08/28/revised PY - 2020/09/16/accepted PY - 2020/10/5/entrez PY - 2020/10/6/pubmed PY - 2020/10/9/medline SP - 9756518 EP - 9756518 JF - Computational and mathematical methods in medicine JO - Comput Math Methods Med VL - 2020 N2 - The COVID-19 diagnostic approach is mainly divided into two broad categories, a laboratory-based and chest radiography approach. The last few months have witnessed a rapid increase in the number of studies use artificial intelligence (AI) techniques to diagnose COVID-19 with chest computed tomography (CT). In this study, we review the diagnosis of COVID-19 by using chest CT toward AI. We searched ArXiv, MedRxiv, and Google Scholar using the terms "deep learning", "neural networks", "COVID-19", and "chest CT". At the time of writing (August 24, 2020), there have been nearly 100 studies and 30 studies among them were selected for this review. We categorized the studies based on the classification tasks: COVID-19/normal, COVID-19/non-COVID-19, COVID-19/non-COVID-19 pneumonia, and severity. The sensitivity, specificity, precision, accuracy, area under the curve, and F1 score results were reported as high as 100%, 100%, 99.62, 99.87%, 100%, and 99.5%, respectively. However, the presented results should be carefully compared due to the different degrees of difficulty of different classification tasks. SN - 1748-6718 UR - https://www.unboundmedicine.com/medline/citation/33014121/Review_on_Diagnosis_of_COVID_19_from_Chest_CT_Images_Using_Artificial_Intelligence_ L2 - https://doi.org/10.1155/2020/9756518 DB - PRIME DP - Unbound Medicine ER -