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Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features.
Adv Exp Med Biol. 2021; 1327:139-147.AE

Abstract

Background and aims Non-contrast chest computed tomography (CT) scanning is one of the important tools for evaluating of lung lesions. The aim of this study was to use a deep learning approach for predicting the outcome of patients with COVID-19 into two groups of critical and non-critical according to their CT features. Methods This was carried out as a retrospective study from March to April 2020 in Baqiyatallah Hospital, Tehran, Iran. From total of 1078 patients with COVID-19 pneumonia who underwent chest CT, 169 were critical cases and 909 were non-critical. Deep learning neural networks were used to classify samples into critical or non-critical ones according to the chest CT results. Results The best accuracy of prediction was seen by the presence of diffuse opacities and lesion distribution (both=0.91, 95% CI: 0.83-0.99). The largest sensitivity was achieved using lesion distribution (0.74, 95% CI: 0.55-0.93), and the largest specificity was for presence of diffuse opacities (0.95, 95% CI: 0.9-1). The total model showed an accuracy of 0.89 (95% CI: 0.79-0.99), and the corresponding sensitivity and specificity were 0.71 (95% CI: 0.51-0.91) and 0.93 (95% CI: 0.87-0.96), respectively. Conclusions The results showed that CT scan can accurately classify and predict critical and non-critical COVID-19 cases.

Authors+Show Affiliations

Department of Biostatistics, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran.Department of Biostatistics, University of Social Welfare and Rehabilitation Sciences, Tehran, Iran.Eye Research Center, The five Senses Institute, Rassoul Akram Hospital, Iran University of Medical Sciences, Tehran, Iran. smrchaibakhsh@gmail.com.Gastroenterology and Liver Diseases Research Center, Research Institute for Gastroenterology and Liver Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran.Trauma Research Center, Nursing Faculty, Baqiyatallah University of Medical Sciences, Tehran, Iran.Laboratory of Neuroproteomics, Department of Biochemistry and Tissue Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil.Anesthesia and Critical Care Department, Hamadan University of Medical Sciences, Hamadan, Iran.Biotechnology Research Center, Pharmaceutical Technology Institute, Mashhad University of Medical Sciences, Mashhad, Iran. amir_saheb2000@yahoo.com. Applied Biomedical Research Center, Mashhad University of Medical Sciences, Mashhad, Iran. amir_saheb2000@yahoo.com. Polish Mother's Memorial Hospital Research Institute (PMMHRI), Lodz, Poland. amir_saheb2000@yahoo.com. School of Pharmacy, Mashhad University of Medical Sciences, Mashhad, Iran. amir_saheb2000@yahoo.com.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

34279835

Citation

Pourhoseingholi, Asma, et al. "Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features." Advances in Experimental Medicine and Biology, vol. 1327, 2021, pp. 139-147.
Pourhoseingholi A, Vahedi M, Chaibakhsh S, et al. Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features. Adv Exp Med Biol. 2021;1327:139-147.
Pourhoseingholi, A., Vahedi, M., Chaibakhsh, S., Pourhoseingholi, M. A., Vahedian-Azimi, A., Guest, P. C., Rahimi-Bashar, F., & Sahebkar, A. (2021). Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features. Advances in Experimental Medicine and Biology, 1327, 139-147. https://doi.org/10.1007/978-3-030-71697-4_11
Pourhoseingholi A, et al. Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features. Adv Exp Med Biol. 2021;1327:139-147. PubMed PMID: 34279835.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features. AU - Pourhoseingholi,Asma, AU - Vahedi,Mohsen, AU - Chaibakhsh,Samira, AU - Pourhoseingholi,Mohamad Amin, AU - Vahedian-Azimi,Amir, AU - Guest,Paul C, AU - Rahimi-Bashar,Farshid, AU - Sahebkar,Amirhossein, PY - 2021/7/19/entrez PY - 2021/7/20/pubmed PY - 2021/7/22/medline KW - COVID-2019 KW - Chest CT scan KW - Computed tomography KW - Deep learning KW - Prediction SP - 139 EP - 147 JF - Advances in experimental medicine and biology JO - Adv Exp Med Biol VL - 1327 N2 - Background and aims Non-contrast chest computed tomography (CT) scanning is one of the important tools for evaluating of lung lesions. The aim of this study was to use a deep learning approach for predicting the outcome of patients with COVID-19 into two groups of critical and non-critical according to their CT features. Methods This was carried out as a retrospective study from March to April 2020 in Baqiyatallah Hospital, Tehran, Iran. From total of 1078 patients with COVID-19 pneumonia who underwent chest CT, 169 were critical cases and 909 were non-critical. Deep learning neural networks were used to classify samples into critical or non-critical ones according to the chest CT results. Results The best accuracy of prediction was seen by the presence of diffuse opacities and lesion distribution (both=0.91, 95% CI: 0.83-0.99). The largest sensitivity was achieved using lesion distribution (0.74, 95% CI: 0.55-0.93), and the largest specificity was for presence of diffuse opacities (0.95, 95% CI: 0.9-1). The total model showed an accuracy of 0.89 (95% CI: 0.79-0.99), and the corresponding sensitivity and specificity were 0.71 (95% CI: 0.51-0.91) and 0.93 (95% CI: 0.87-0.96), respectively. Conclusions The results showed that CT scan can accurately classify and predict critical and non-critical COVID-19 cases. SN - 0065-2598 UR - https://www.unboundmedicine.com/medline/citation/34279835/Deep_Learning_Analysis_in_Prediction_of_COVID-19_Infection_Status_Using_Chest_CT_Scan_Features. L2 - https://dx.doi.org/10.1007/978-3-030-71697-4_11 DB - PRIME DP - Unbound Medicine ER -