Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography.
Sci Rep
; 10(1): 19196, 2020 11 05.
Article
in English
| MEDLINE | ID: covidwho-912912
ABSTRACT
Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system based on deep learning for detecting COVID-19 pneumonia on high resolution CT. For model development and validation, 46,096 anonymous images from 106 admitted patients, including 51 patients of laboratory confirmed COVID-19 pneumonia and 55 control patients of other diseases in Renmin Hospital of Wuhan University were retrospectively collected. Twenty-seven prospective consecutive patients in Renmin Hospital of Wuhan University were collected to evaluate the efficiency of radiologists against 2019-CoV pneumonia with that of the model. An external test was conducted in Qianjiang Central Hospital to estimate the system's robustness. The model achieved a per-patient accuracy of 95.24% and a per-image accuracy of 98.85% in internal retrospective dataset. For 27 internal prospective patients, the system achieved a comparable performance to that of expert radiologist. In external dataset, it achieved an accuracy of 96%. With the assistance of the model, the reading time of radiologists was greatly decreased by 65%. The deep learning model showed a comparable performance with expert radiologist, and greatly improved the efficiency of radiologists in clinical practice.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Pneumonia
/
Pneumonia, Viral
/
Image Processing, Computer-Assisted
/
Tomography, X-Ray Computed
/
Coronavirus Infections
/
Signal-To-Noise Ratio
/
Deep Learning
Type of study:
Experimental Studies
/
Observational study
/
Prognostic study
Topics:
Long Covid
Limits:
Adult
/
Female
/
Humans
/
Male
/
Middle aged
Language:
English
Journal:
Sci Rep
Year:
2020
Document Type:
Article
Affiliation country:
S41598-020-76282-0
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