A deep-learning pipeline for the diagnosis and discrimination of viral, non-viral and COVID-19 pneumonia from chest X-ray images.
Guangyu WangXiaohong LiuJun ShenChengdi WangZhihuan LiLinsen YeXingwang WuTing ChenKai WangXuan ZhangZhongguo ZhouJian YangYe SangRuiyun DengWenhua LiangTao YuMing GaoJin WangZehong YangHuimin CaiGuang Ming LuLingyan ZhangLei YangWenqin XuWinston T WangAndrea OlveraIan ZiyarCharlotte ZhangOulan LiWeihua LiaoYanyao DuWen ChenWei ChenJichan ShiLianghong ZhengLongjiang ZhangZhihan YanXiaoguang ZouGuiping LinGuiqun CaoLaurance L LauLong MoYong LiangMichael RobertsEvis SalaCarola-Bibiane SchönliebManson FokJohnson Yiu-Nam LauTao XuJianxing HeKang ZhangWeimin LiTianxin LinPublished in: Nature biomedical engineering (2021)
Common lung diseases are first diagnosed using chest X-rays. Here, we show that a fully automated deep-learning pipeline for the standardization of chest X-ray images, for the visualization of lesions and for disease diagnosis can identify viral pneumonia caused by coronavirus disease 2019 (COVID-19) and assess its severity, and can also discriminate between viral pneumonia caused by COVID-19 and other types of pneumonia. The deep-learning system was developed using a heterogeneous multicentre dataset of 145,202 images, and tested retrospectively and prospectively with thousands of additional images across four patient cohorts and multiple countries. The system generalized across settings, discriminating between viral pneumonia, other types of pneumonia and the absence of disease with areas under the receiver operating characteristic curve (AUCs) of 0.94-0.98; between severe and non-severe COVID-19 with an AUC of 0.87; and between COVID-19 pneumonia and other viral or non-viral pneumonia with AUCs of 0.87-0.97. In an independent set of 440 chest X-rays, the system performed comparably to senior radiologists and improved the performance of junior radiologists. Automated deep-learning systems for the assessment of pneumonia could facilitate early intervention and provide support for clinical decision-making.
Keyphrases
- deep learning
- sars cov
- coronavirus disease
- artificial intelligence
- convolutional neural network
- respiratory syndrome coronavirus
- machine learning
- respiratory failure
- community acquired pneumonia
- early onset
- high resolution
- magnetic resonance
- extracorporeal membrane oxygenation
- mass spectrometry
- acute respiratory distress syndrome
- cross sectional