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01-09-2021 | Computed Tomography | Chest

AI detection of mild COVID-19 pneumonia from chest CT scans

Authors: Jin-Cao Yao, Tao Wang, Guang-Hua Hou, Di Ou, Wei Li, Qiao-Dan Zhu, Wen-Cong Chen, Chen Yang, Li-Jing Wang, Li-Ping Wang, Lin-Yin Fan, Kai-Yuan Shi, Jie Zhang, Dong Xu, Ya-Qing Li

Published in: European Radiology | Issue 9/2021

Abstract

Objectives

An artificial intelligence model was adopted to identify mild COVID-19 pneumonia from computed tomography (CT) volumes, and its diagnostic performance was then evaluated.

Methods

In this retrospective multicenter study, an atrous convolution-based deep learning model was established for the computer-assisted diagnosis of mild COVID-19 pneumonia. The dataset included 2087 chest CT exams collected from four hospitals between 1 January 2019 and 31 May 2020. The true positive rate, true negative rate, receiver operating characteristic curve, area under the curve (AUC) and convolutional feature map were used to evaluate the model.

Results

The proposed deep learning model was trained on 1538 patients and tested on an independent testing cohort of 549 patients. The overall sensitivity was 91.5% (195/213; p < 0.001, 95% CI: 89.2–93.9%), the overall specificity was 90.5% (304/336; p < 0.001, 95% CI: 88.0–92.9%) and the general AUC value was 0.955 (p < 0.001).

Conclusions

A deep learning model can accurately detect COVID-19 and serve as an important supplement to the COVID-19 reverse transcription–polymerase chain reaction (RT-PCR) test.

Key Points

• The implementation of a deep learning model to identify mild COVID-19 pneumonia was confirmed to be effective and feasible.

• The strategy of using a binary code instead of the region of interest label to identify mild COVID-19 pneumonia was verified.

• This AI model can assist in the early screening of COVID-19 without interfering with normal clinical examinations.

Available only for authorised users

Our previous CSAC-Net was submitted to PASCAL VOC server on 9 February 2020 and ranked first in the object classification task (Supplementary Materials 1).

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Title: AI detection of mild COVID-19 pneumonia from chest CT scans
Authors: Jin-Cao Yao
Tao Wang
Guang-Hua Hou
Di Ou
Wei Li
Qiao-Dan Zhu
Wen-Cong Chen
Chen Yang
Li-Jing Wang
Li-Ping Wang
Lin-Yin Fan
Kai-Yuan Shi
Jie Zhang
Dong Xu
Ya-Qing Li
Publication date: 01-09-2021
Publisher: Springer Berlin Heidelberg
Keywords: Computed Tomography
Computed Tomography
Artificial Intelligence
Community-Acquired Pneumonia
Pneumonia
COVID-19
Published in: European Radiology / Issue 9/2021
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-021-07797-x

At a glance: The STEP trials

Springer Medicine

AI detection of mild COVID-19 pneumonia from chest CT scans

Abstract

Objectives

Methods

Results

Conclusions

Key Points

At a glance: The STEP trials

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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