人工智能对乳腺x光筛查的影响。

IF 2.9
Breast cancer (Tokyo, Japan) Pub Date : 2022-11-01 Epub Date: 2022-06-28 DOI:10.1007/s12282-022-01375-9
Lan-Anh Dang, Emmanuel Chazard, Edouard Poncelet, Teodora Serb, Aniela Rusu, Xavier Pauwels, Clémence Parsy, Thibault Poclet, Hugo Cauliez, Constance Engelaere, Guillaume Ramette, Charlotte Brienne, Sofiane Dujardin, Nicolas Laurent
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引用次数: 9

摘要

目的:证明放射科医生在人工智能(AI)的帮助下,能够更好地将筛查乳房x光片分类到正确的乳房成像报告和数据系统(BI-RADS)类别中,并作为次要目标,探讨AI对癌症检测和乳房x光片解释时间的影响。方法:采用交叉设计的多读者、多病例研究,包括314张乳房x线照片。在有和没有人工智能支持的情况下,12名放射科医生在两次会议中解释了4周的洗脱期。对于每次乳房x光检查的每个乳房,他们必须标记最可疑的病变(如果有的话),并将其分配给强制BI-RADS类别和可疑水平或“连续BI-RADS 100”。以评估每个乳房BI-RADS类别的观察者间一致性的Cohen's kappa相关系数和受试者工作特征曲线下的面积(AUC)作为指标并进行分析。结果:平均而言,使用人工智能时,所有读者的二次卡帕系数显著增加[未使用人工智能时κ = 0.549, 95% CI(0.528-0.571),使用人工智能时κ = 0.626, 95% CI(0.607-0.6455)]。人工智能显著改善了AUC (0.74 vs 0.77, p = 0.004)。所有读者的阅读时间都没有明显的影响(没有人工智能的106秒和有人工智能的102秒;p = 0.754)。结论:当使用人工智能时,放射科医生能够更好地分配具有正确BI-RADS类别的乳房x线照片,而不会减慢解释时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Impact of artificial intelligence in breast cancer screening with mammography.

Impact of artificial intelligence in breast cancer screening with mammography.

Impact of artificial intelligence in breast cancer screening with mammography.

Impact of artificial intelligence in breast cancer screening with mammography.

Objectives: To demonstrate that radiologists, with the help of artificial intelligence (AI), are able to better classify screening mammograms into the correct breast imaging reporting and data system (BI-RADS) category, and as a secondary objective, to explore the impact of AI on cancer detection and mammogram interpretation time.

Methods: A multi-reader, multi-case study with cross-over design, was performed, including 314 mammograms. Twelve radiologists interpreted the examinations in two sessions delayed by a 4 weeks wash-out period with and without AI support. For each breast of each mammogram, they had to mark the most suspicious lesion (if any) and assign it with a forced BI-RADS category and a level of suspicion or "continuous BI-RADS 100". Cohen's kappa correlation coefficient evaluating the inter-observer agreement for BI-RADS category per breast, and the area under the receiver operating characteristic curve (AUC), were used as metrics and analyzed.

Results: On average, the quadratic kappa coefficient increased significantly when using AI for all readers [κ = 0.549, 95% CI (0.528-0.571) without AI and κ = 0.626, 95% CI (0.607-0.6455) with AI]. AUC was significantly improved when using AI (0.74 vs 0.77, p = 0.004). Reading time was not significantly affected for all readers (106 s without AI and vs 102 s with AI; p = 0.754).

Conclusions: When using AI, radiologists were able to better assign mammograms with the correct BI-RADS category without slowing down the interpretation time.

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