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01-12-2020 | Coronavirus | Computed Tomography

A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images

Authors: Qianqian Ni, Zhi Yuan Sun, Li Qi, Wen Chen, Yi Yang, Li Wang, Xinyuan Zhang, Liu Yang, Yi Fang, Zijian Xing, Zhen Zhou, Yizhou Yu, Guang Ming Lu, Long Jiang Zhang

Published in: European Radiology | Issue 12/2020

Abstract

Objectives

To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents.

Methods

A deep learning algorithm consisted of lesion detection, segmentation, and location was trained and validated in 14,435 participants with chest CT images and definite pathogen diagnosis. The algorithm was tested in a non-overlapping dataset of 96 confirmed COVID-19 patients in three hospitals across China during the outbreak. Quantitative detection performance of the model was compared with three radiological residents with two experienced radiologists’ reading reports as reference standard by assessing the accuracy, sensitivity, specificity, and F1 score.

Results

Of 96 patients, 88 had pneumonia lesions on CT images and 8 had no abnormities on CT images. For per-patient basis, the algorithm showed superior sensitivity of 1.00 (95% confidence interval (CI) 0.95, 1.00) and F1 score of 0.97 in detecting lesions from CT images of COVID-19 pneumonia patients. While for per-lung lobe basis, the algorithm achieved a sensitivity of 0.96 (95% CI 0.94, 0.98) and a slightly inferior F1 score of 0.86. The median volume of lesions calculated by algorithm was 40.10 cm³. An average running speed of 20.3 s ± 5.8 per case demonstrated the algorithm was much faster than the residents in assessing CT images (all p < 0.017). The deep learning algorithm can also assist radiologists make quicker diagnosis (all p < 0.0001) with superior diagnostic performance.

Conclusions

The algorithm showed excellent performance in detecting COVID-19 pneumonia on chest CT images compared with resident radiologists.

Key Points

• The higher sensitivity of deep learning model in detecting COVID-19 pneumonia were found compared with radiological residents on a per-lobe and per-patient basis.

• The deep learning model improves diagnosis efficiency by shortening processing time.

• The deep learning model can automatically calculate the volume of the lesions and whole lung.

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Title: A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images
Authors: Qianqian Ni
Zhi Yuan Sun
Li Qi
Wen Chen
Yi Yang
Li Wang
Xinyuan Zhang
Liu Yang
Yi Fang
Zijian Xing
Zhen Zhou
Yizhou Yu
Guang Ming Lu
Long Jiang Zhang
Publication date: 01-12-2020
Publisher: Springer Berlin Heidelberg
Keywords: Coronavirus
Artificial Intelligence
Pneumonia
COVID-19
Published in: European Radiology / Issue 12/2020
Print ISSN: 0938-7994
Electronic ISSN: 1432-1084
DOI: https://doi.org/10.1007/s00330-020-07044-9

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images

Abstract

Objectives

Methods

Results

Conclusions

Key Points

At a glance: The ONWARDS insulin icodec trials

Springer Medicine

Abstract

Objectives

Methods

Results

Conclusions

Key Points

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