Machine learning methods offer great promise for fast and accurate detection and prognostication of coronavirus disease 2019 (COVID-19) from standard-of-care chest radiographs (CXR) and chest computed tomography (CT) images. Many articles have been published in 2020 describing new machine learning-based models for both of these tasks, but it is unclear which are of potential clinical utility. In this systematic review, we consider all published papers and preprints, for the period from 1 January 2020 to 3 October 2020, which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. All manuscripts uploaded to bioRxiv, medRxiv and arXiv along with all entries in EMBASE and MEDLINE in this timeframe are considered. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 62 studies were included in this systematic review. Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases. This is a major weakness, given the urgency with which validated COVID-19 models are needed. To address this, we give many recommendations which, if followed, will solve these issues and lead to higher-quality model development and well-documented manuscripts.

机器学习方法为从标准护理胸片 (CXR) 和胸部计算机断层扫描 (CT) 图像中快速准确地检测和预测 2019 年冠状病毒病 (COVID-19) 提供了巨大的希望。2020 年发表了许多文章，描述了用于这两项任务的基于机器学习的新模型，但尚不清楚哪些具有潜在的临床实用性。在本系统综述中，我们考虑了 2020 年 1 月 1 日至 2020 年 10 月 3 日期间所有已发表的论文和预印本，它们描述了从 CXR 或 CT 图像诊断或预后 COVID-19 的新机器学习模型。在此时间范围内上传到 bioRxiv、medRxiv 和 arXiv 的所有手稿以及 EMBASE 和 MEDLINE 中的所有条目都会被考虑。我们的搜索确定了 2,212 项研究，其中 415 项在初步筛选后被纳入，并且，经过质量筛选，62 项研究被纳入本系统评价。我们的审查发现，由于方法学缺陷和/或潜在的偏见，所确定的模型都没有潜在的临床用途。鉴于迫切需要经过验证的 COVID-19 模型，这是一个主要弱点。为了解决这个问题，我们提出了许多建议，如果遵循这些建议，将解决这些问题并带来更高质量的模型开发和有据可查的手稿。