Borkowski Andrew A, Viswanadhan Narayan A, Thomas L Brannon, Guzman Rodney D, Deland Lauren A, Mastorides Stephen M
is Chief of the Molecular Diagnostics Laboratory, is Chief of the Microbiology Laboratory, is a Research Coordinator, and is Chief of Pathology; is Assistant Chief of Radiology; all at the James A. Haley Veterans' Hospital in Tampa, Florida. is a Cofounder of InterKnowlogy, LLC in Carlsbad, California. Andrew Borkowski and Stephen Mastorides are Professors and L. Brannon Thomas is an Assistant Professor, all in the Department of Pathology and Cell Biology, University of South Florida, Morsani College of Medicine in Tampa, Florida.
Fed Pract. 2020 Sep;37(9):398-404. doi: 10.12788/fp.0045.
Coronavirus disease-19 (COVID-19), caused by a novel member of the coronavirus family, is a respiratory disease that rapidly reached pandemic proportions with high morbidity and mortality. In only a few months, it has had a dramatic impact on society and world economies. COVID-19 has presented numerous challenges to all aspects of health care, including reliable methods for diagnosis, treatment, and prevention. Initial efforts to contain the spread of the virus were hampered by the time required to develop reliable diagnostic methods. Artificial intelligence (AI) is a rapidly growing field of computer science with many applications for health care. Machine learning is a subset of AI that uses deep learning with neural network algorithms. It can recognize patterns and achieve complex computational tasks often far quicker and with increased precision than can humans.
In this article, we explore the potential for the simple and widely available chest X-ray (CXR) to be used with AI to diagnose COVID-19 reliably. Microsoft CustomVision is an automated image classification and object detection system that is a part of Microsoft Azure Cognitive Services. We utilized publicly available CXR images for patients with COVID-19 pneumonia, pneumonia from other etiologies, and normal CXRs as a dataset to train Microsoft CustomVision.
Our trained model overall demonstrated 92.9% sensitivity (recall) and positive predictive value (precision), with results for each label showing sensitivity and positive predictive value at 94.8% and 98.9% for COVID-19 pneumonia, 89% and 91.8% for non-COVID-19 pneumonia, 95% and 88.8% for normal lung. We then validated the program using CXRs of patients from our institution with confirmed COVID-19 diagnoses along with non-COVID-19 pneumonia and normal CXRs. Our model performed with 100% sensitivity, 95% specificity, 97% accuracy, 91% positive predictive value, and 100% negative predictive value.
We have used a readily available, commercial platform to demonstrate the potential of AI to assist in the successful diagnosis of COVID-19 pneumonia on CXR images. The findings have implications for screening and triage, initial diagnosis, monitoring disease progression, and identifying patients at increased risk of morbidity and mortality. Based on the data, a website was created to demonstrate how such technologies could be shared and distributed to others to combat entities such as COVID-19 moving forward.
新型冠状病毒肺炎（COVID-19）由冠状病毒家族的一个新成员引起，是一种迅速蔓延至全球大流行规模、发病率和死亡率都很高的呼吸道疾病。在短短几个月内，它对社会和世界经济产生了巨大影响。COVID-19在医疗保健的各个方面都带来了诸多挑战，包括可靠的诊断、治疗和预防方法。最初控制病毒传播的努力因开发可靠诊断方法所需的时间而受阻。人工智能（AI）是计算机科学中一个快速发展的领域，在医疗保健中有许多应用。机器学习是AI的一个子集，它使用带有神经网络算法的深度学习。它能够识别模式并完成复杂的计算任务，通常比人类更快且精度更高。
在本文中，我们探讨了简单且广泛可用的胸部X线（CXR）与AI结合使用以可靠诊断COVID-19的潜力。微软自定义视觉是一个自动图像分类和目标检测系统，是微软Azure认知服务的一部分。我们将公开可用的COVID-19肺炎患者、其他病因引起的肺炎患者以及正常CXR的图像作为数据集来训练微软自定义视觉。
我们训练的模型总体显示出92.9%的灵敏度（召回率）和阳性预测值（精确率），每个标签的结果显示，COVID-19肺炎的灵敏度和阳性预测值分别为94.8%和98.9%，非COVID-19肺炎为89%和91.8%，正常肺部为95%和88.8%。然后，我们使用本机构确诊为COVID-19以及非COVID-19肺炎和正常CXR的患者的CXR对该程序进行了验证。我们的模型表现出100%的灵敏度、95%的特异性、97%的准确率、91%的阳性预测值和100%的阴性预测值。
我们使用了一个现成的商业平台来证明AI在协助通过CXR图像成功诊断COVID-19肺炎方面的潜力。这些发现对筛查和分诊、初步诊断、监测疾病进展以及识别发病和死亡风险增加的患者具有重要意义。基于这些数据，创建了一个网站来展示如何将此类技术分享和分发给其他机构，以应对未来类似COVID-19这样的情况。
