小柯机器人

人工智能实现对乳腺癌的筛查
2020-01-02 15:19

谷歌健康Shravya Shetty、Daniel Tse、Scott Mayer McKinney等研究人员建立了能够对乳腺癌进行筛查的人工智能系统。2020年1月1日,国际知名学术期刊《自然》在线发表了这一成果。

研究人员提出了一种人工智能(AI)系统,该系统能够在乳腺癌预测方面超越人类专家。为了评估其在临床环境中的性能,研究人员选择了来自英国的大型代表性数据集和来自美国的大型丰富数据集。研究人员发现假阳性绝对值降低了5.7%和1.2%(美国和英国),假阴性绝对值降低了9.4%和2.7%。研究人员提供了可将该系统从英国推广到美国的证据。在对六位放射科医生的独立研究中,人工智能系统的表现优于所有人类专家:人工智能系统在接收器工作特性曲线下的面积(AUC-ROC)比一般放射线医师的AUC-ROC绝对幅度大了11.5%。研究人员进行了模拟,其中AI系统参与了在英国使用的双重判断过程,结果发现AI系统保持了不逊色的性能并将第二重判断的工作量减少了88%。AI系统的强大评估为临床试验铺平了道路,从而可提高乳腺癌筛查的准确性和效率。

据了解,乳腺钼靶筛查的目的是在疾病较早的阶段识别乳腺癌,从而更成功地进行治疗。尽管全世界都存在筛查程序,但乳房X光照片的判断存在较高的假阳性和假阴性。

附:英文原文

Title: International evaluation of an AI system for breast cancer screening

Author: Scott Mayer McKinney, Marcin Sieniek, Varun Godbole, Jonathan Godwin, Natasha Antropova, Hutan Ashrafian, Trevor Back, Mary Chesus, Greg C. Corrado, Ara Darzi, Mozziyar Etemadi, Florencia Garcia-Vicente, Fiona J. Gilbert, Mark Halling-Brown, Demis Hassabis, Sunny Jansen, Alan Karthikesalingam, Christopher J. Kelly, Dominic King, Joseph R. Ledsam, David Melnick, Hormuz Mostofi, Lily Peng, Joshua Jay Reicher, Bernardino Romera-Paredes, Richard Sidebottom, Mustafa Suleyman, Daniel Tse, Kenneth C. Young, Jeffrey De Fauw, Shravya Shetty

Issue&Volume: 2020-01-01

Abstract: Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful1. Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives2. Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.

DOI: 10.1038/s41586-019-1799-6

Source: https://www.nature.com/articles/s41586-019-1799-6

Nature:《自然》,创刊于1869年。隶属于施普林格·自然出版集团,最新IF:43.07
官方网址:http://www.nature.com/
投稿链接:http://www.nature.com/authors/submit_manuscript.html


本期文章:《自然》:Online/在线发表

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