小柯机器人

美国西奈山伊坎医学院Yang Yang、Zahi A. Fayad、麻省总医院Brent P. Little等研究人员，合作利用人工智能实现对新冠肺炎患者的快速诊断。2020年5月19日，《自然—医学》杂志在线发表了这一研究成果。

据研究人员介绍，2019冠状病毒疾病（COVID-19）的诊断通常使用SARS-CoV-2病毒特异性逆转录酶聚合酶链反应（RT-PCR）测试。但是，此测试最多可能需要2天的时间才能完成，还可能需要进行串行测试以排除假阴性结果，并且目前缺少RT–PCR测试试剂盒，这突显了对快速检测并准确诊断COVID-19患者的迫切需求。胸部计算机断层扫描（CT）是评估疑似SARS-CoV-2感染患者的重要手段。尽管如此，只用CT对于判断SARS-CoV-2感染的价值仍有限，因为某些患者在疾病早期可能具有正常的表现。

Author: Xueyan Mei, Hao-Chih Lee, Kai-yue Diao, Mingqian Huang, Bin Lin, Chenyu Liu, Zongyu Xie, Yixuan Ma, Philip M. Robson, Michael Chung, Adam Bernheim, Venkatesh Mani, Claudia Calcagno, Kunwei Li, Shaolin Li, Hong Shan, Jian Lv, Tongtong Zhao, Junli Xia, Qihua Long, Sharon Steinberger, Adam Jacobi, Timothy Deyer, Marta Luksza, Fang Liu, Brent P. Little, Zahi A. Fayad, Yang Yang

Issue&Volume: 2020-05-19

Abstract: For diagnosis of coronavirus disease 2019 (COVID-19), a SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT–PCR) test is routinely used. However, this test can take up to 2d to complete, serial testing may be required to rule out the possibility of false negative results and there is currently a shortage of RT–PCR test kits, underscoring the urgent need for alternative methods for rapid and accurate diagnosis of patients with COVID-19. Chest computed tomography (CT) is a valuable component in the evaluation of patients with suspected SARS-CoV-2 infection. Nevertheless, CT alone may have limited negative predictive value for ruling out SARS-CoV-2 infection, as some patients may have normal radiological findings at early stages of the disease. In this study, we used artificial intelligence (AI) algorithms to integrate chest CT findings with clinical symptoms, exposure history and laboratory testing to rapidly diagnose patients who are positive for COVID-19. Among a total of 905 patients tested by real-time RT–PCR assay and next-generation sequencing RT–PCR, 419 (46.3%) tested positive for SARS-CoV-2. In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist. The AI system also improved the detection of patients who were positive for COVID-19 via RT–PCR who presented with normal CT scans, correctly identifying 17 of 25 (68%) patients, whereas radiologists classified all of these patients as COVID-19 negative. When CT scans and associated clinical history are available, the proposed AI system can help to rapidly diagnose COVID-19 patients.

DOI: 10.1038/s41591-020-0931-3

Source: https://www.nature.com/articles/s41591-020-0931-3

Nature Medicine：《自然—医学》，创刊于1995年。隶属于施普林格·自然出版集团，最新IF：87.241
官方网址：https://www.nature.com/nm/
投稿链接：https://mts-nmed.nature.com/cgi-bin/main.plex