Deep Learning Analysis in Prediction of COVID-19 Infection Status Using Chest CT Scan Features.
Adv Exp Med Biol
; 1327: 139-147, 2021.
Article
in English
| MEDLINE | ID: covidwho-1316244
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Deep Learning
/
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
/
Observational study
/
Prognostic study
/
Randomized controlled trials
Limits:
Humans
Country/Region as subject:
Asia
Language:
English
Journal:
Adv Exp Med Biol
Year:
2021
Document Type:
Article
Affiliation country:
978-3-030-71697-4_11
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