Li Dongguang, Li Shaoguang
Division of Hematology/Oncology, Department of Medicine, University of Massachusetts Medical School, Worcester, MA, USA.
iScience. 2022 Apr 15;25(4):104031. doi: 10.1016/j.isci.2022.104031. Epub 2022 Mar 6.
The coronavirus disease of 2019 (Covid-19) causes deadly lung infections (pneumonia). Accurate clinical diagnosis of Covid-19 is essential for guiding treatment. Covid-19 RNA test does not reflect clinical features and severity of the disease. Pneumonia in Covid-19 patients could be caused by non-Covid-19 organisms and distinguishing Covid-19 pneumonia from non-Covid-19 pneumonia is critical. Chest X-ray detects pneumonia, but a high diagnostic accuracy is difficult to achieve. We develop an artificial intelligence-based (AI) deep learning method with a high diagnostic accuracy for Covid-19 pneumonia. We analyzed 10,182 chest X-ray images of healthy individuals, bacterial pneumonia. and viral pneumonia (Covid-19 and non-Covid-19) to build and test AI models. Among viral pneumonia, diagnostic accuracy for Covid-19 reaches 99.95%. High diagnostic accuracy is also achieved for distinguishing Covid-19 pneumonia from bacterial pneumonia (99.85% accuracy) or normal lung images (100% accuracy). Our AI models are accurate for clinical diagnosis of Covid-19 pneumonia by reading solely chest X-ray images.
2019年冠状病毒病（Covid-19）可引发致命的肺部感染（肺炎）。准确的Covid-19临床诊断对于指导治疗至关重要。Covid-19 RNA检测并不能反映该疾病的临床特征和严重程度。Covid-19患者的肺炎可能由非Covid-19病原体引起，区分Covid-19肺炎和非Covid-19肺炎至关重要。胸部X光可检测出肺炎，但难以实现高诊断准确率。我们开发了一种基于人工智能（AI）的深度学习方法，用于诊断Covid-19肺炎，具有很高的诊断准确率。我们分析了10182张健康个体、细菌性肺炎和病毒性肺炎（Covid-19和非Covid-19）的胸部X光图像，以构建和测试AI模型。在病毒性肺炎中，对Covid-19的诊断准确率达到99.95%。在区分Covid-19肺炎与细菌性肺炎（准确率99.85%）或正常肺部图像（准确率100%）方面也实现了高诊断准确率。我们的AI模型仅通过读取胸部X光图像就能准确诊断Covid-19肺炎。
