A Semiautonomous Deep Learning System to Reduce False Positives in Screening Mammography.

IF 8.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Radiology-Artificial Intelligence Pub Date : 2024-05-01 DOI:10.1148/ryai.230033
Stefano Pedemonte, Trevor Tsue, Brent Mombourquette, Yen Nhi Truong Vu, Thomas Matthews, Rodrigo Morales Hoil, Meet Shah, Nikita Ghare, Naomi Zingman-Daniels, Susan Holley, Catherine M Appleton, Jason Su, Richard L Wahl
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Abstract

Purpose To evaluate the ability of a semiautonomous artificial intelligence (AI) model to identify screening mammograms not suspicious for breast cancer and reduce the number of false-positive examinations. Materials and Methods The deep learning algorithm was trained using 123 248 two-dimensional digital mammograms (6161 cancers) and a retrospective study was performed on three nonoverlapping datasets of 14 831 screening mammography examinations (1026 cancers) from two U.S. institutions and one U.K. institution (2008-2017). The stand-alone performance of humans and AI was compared. Human plus AI performance was simulated to examine reductions in the cancer detection rate, number of examinations, false-positive callbacks, and benign biopsies. Metrics were adjusted to mimic the natural distribution of a screening population, and bootstrapped CIs and P values were calculated. Results Retrospective evaluation on all datasets showed minimal changes to the cancer detection rate with use of the AI device (noninferiority margin of 0.25 cancers per 1000 examinations: U.S. dataset 1, P = .02; U.S. dataset 2, P < .001; U.K. dataset, P < .001). On U.S. dataset 1 (11 592 mammograms; 101 cancers; 3810 female patients; mean age, 57.3 years ± 10.0 [SD]), the device reduced screening examinations requiring radiologist interpretation by 41.6% (95% CI: 40.6%, 42.4%; P < .001), diagnostic examinations callbacks by 31.1% (95% CI: 28.7%, 33.4%; P < .001), and benign needle biopsies by 7.4% (95% CI: 4.1%, 12.4%; P < .001). U.S. dataset 2 (1362 mammograms; 330 cancers; 1293 female patients; mean age, 55.4 years ± 10.5) was reduced by 19.5% (95% CI: 16.9%, 22.1%; P < .001), 11.9% (95% CI: 8.6%, 15.7%; P < .001), and 6.5% (95% CI: 0.0%, 19.0%; P = .08), respectively. The U.K. dataset (1877 mammograms; 595 cancers; 1491 female patients; mean age, 63.5 years ± 7.1) was reduced by 36.8% (95% CI: 34.4%, 39.7%; P < .001), 17.1% (95% CI: 5.9%, 30.1%: P < .001), and 5.9% (95% CI: 2.9%, 11.5%; P < .001), respectively. Conclusion This work demonstrates the potential of a semiautonomous breast cancer screening system to reduce false positives, unnecessary procedures, patient anxiety, and medical expenses. Keywords: Artificial Intelligence, Semiautonomous Deep Learning, Breast Cancer, Screening Mammography Supplemental material is available for this article. Published under a CC BY 4.0 license.

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减少乳腺 X 射线筛查假阳性结果的半自主深度学习系统。
"刚刚接受 "的论文经过同行评审,已被接受在《放射学》上发表:人工智能》上发表。这篇文章在以最终版本发表之前,还将经过校对、排版和校对审核。请注意,在制作最终校对稿的过程中,可能会发现一些可能影响内容的错误。目的 评估半自主人工智能(AI)模型识别乳腺癌筛查乳房X光照片的能力,并减少假阳性检查的数量。材料与方法 使用 123,248 张二维数字乳房 X 光照片(6,161 例癌症)对深度学习算法进行了训练,并对来自 2 个美国机构和 1 个英国机构(2008-2017 年)的 3 个非重叠数据集 14,831 例乳房 X 光筛查检查(1,026 例癌症)进行了回顾性研究。比较了人类和人工智能的独立性能。模拟了人类+人工智能的性能,以检查癌症检出率、检查次数、假阳性回调和良性活检的减少情况。对指标进行了调整,以模拟筛查人群的自然分布,并计算了引导置信区间(CI)和 P 值。结果 对所有数据集进行的回顾性评估显示,使用人工智能设备对癌症检出率的影响微乎其微(美国数据集 1 P = .02,美国数据集 2 P < .001,英国 P < .001,非劣效差为每 1000 例检查中发现 0.25 例癌症)。在美国数据集 1(11,592 例乳腺 X 光检查,101 例癌症,3810 名女性患者,平均年龄 57.3 ± [SD] 10.0 岁)中,该设备将需要放射医师判读的筛查减少了 41.6% [95% CI:40.6%, 42.4%] (P < .001),诊断检查回调减少了 31.1% [28.7%, 33.4%] (P < .001),良性针活检减少了 7.4% [4.1%, 12.4%] (P < .001)。美国数据集 2(1362 例乳腺 X 光检查,330 例癌症,1293 例女性患者,平均年龄 55.4 ± 10.5 岁)分别减少了 19.5% [16.9%, 22.1%] (P < .001), 11.9% [8.6%, 15.7%] (P < .001), 和 6.5% [0.0%, 19.0%] (P = .08)。英国数据集(1877 次乳房 X 光检查,595 例癌症,1491 名女性患者,平均年龄为 63.5 ± 7.1 SD)分别减少了 36.8% [34.4%, 39.7%] (P < .001), 17.1% [5.9%, 30.1%] (P < .001), 和 5.9% [2.9%, 11.5%] (P < .001)。结论 这项工作证明了半自主乳腺癌筛查系统在减少假阳性、不必要的手术、患者焦虑和医疗费用方面的潜力。以 CC BY 4.0 许可发布。
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期刊介绍: Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.
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