论文标题
使用机器学习来检测和预测Covid-19的常见陷阱和建议，使用胸部X光片和CT扫描
Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans
论文作者
论文摘要
机器学习方法为快速，准确的检测和预测与护理标准胸部X光片（CXR）和计算机断层扫描（CT）图像对Covid-19提供了巨大的希望。许多文章已经在2020年发表，描述了针对这两个任务的新机器学习模型，但目前尚不清楚哪些具有潜在的临床实用性。在这项系统的综述中，我们通过OVID，通过PubMed，Biorxiv，MedRxiv和Arxiv进行了搜索，以获取已发表的论文和预印本，从2020年1月1日至2020年10月3日上传，描述了从CXR或CT图像中诊断或预后的新机器学习模型。我们的搜索确定了2,212项研究，其中415项在初次筛选后包括了415项，并且在质量筛选后，在此系统审查中包括了61项研究。我们的综述发现，由于方法论缺陷和/或潜在的偏见，所鉴定的任何模型都没有潜在的临床使用。考虑到需要经过验证的COVID-19模型的紧迫性，这是一个主要的弱点。为了解决这个问题，我们提出了许多建议，如果遵循，将解决这些问题并导致更高质量的模型开发和有记录良好的手稿。
Machine learning methods offer great promise for fast and accurate detection and prognostication of COVID-19 from standard-of-care chest radiographs (CXR) and 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 search EMBASE via OVID, MEDLINE via PubMed, bioRxiv, medRxiv and arXiv for published papers and preprints uploaded from January 1, 2020 to October 3, 2020 which describe new machine learning models for the diagnosis or prognosis of COVID-19 from CXR or CT images. Our search identified 2,212 studies, of which 415 were included after initial screening and, after quality screening, 61 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.
