Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams

Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consi...

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Bibliographic Details
Published in:	Nature communications Vol. 12; no. 1; p. 5645
Main Authors:	Shen, Yiqiu, Shamout, Farah E., Oliver, Jamie R., Witowski, Jan, Kannan, Kawshik, Park, Jungkyu, Wu, Nan, Huddleston, Connor, Wolfson, Stacey, Millet, Alexandra, Ehrenpreis, Robin, Awal, Divya, Tyma, Cathy, Samreen, Naziya, Gao, Yiming, Chhor, Chloe, Gandhi, Stacey, Lee, Cindy, Kumari-Subaiya, Sheila, Leonard, Cindy, Mohammed, Reyhan, Moczulski, Christopher, Altabet, Jaime, Babb, James, Lewin, Alana, Reig, Beatriu, Moy, Linda, Heacock, Laura, Geras, Krzysztof J.
Format:	Journal Article
Language:	English
Published:	London Nature Publishing Group UK 24-09-2021 Nature Publishing Group Nature Portfolio
Subjects:	639/705/117 692/308/575 692/4028/67/1347 692/700/1421 Accuracy Adult Aged Algorithms Artificial Intelligence Breast - diagnostic imaging Breast cancer Breast Neoplasms - diagnosis Breast Neoplasms - diagnostic imaging Early Detection of Cancer Female Humanities and Social Sciences Humans Image processing Mammography - methods Medical imaging Middle Aged multidisciplinary Radiologists - statistics & numerical data Reproducibility of Results Retrospective Studies ROC Curve Science Science (multidisciplinary) Ultrasonic imaging Ultrasonography - methods Ultrasound
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Summary:	Though consistently shown to detect mammographically occult cancers, breast ultrasound has been noted to have high false-positive rates. In this work, we present an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images. Developed on 288,767 exams, consisting of 5,442,907 B-mode and Color Doppler images, the AI achieves an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. In a retrospective reader study, the AI achieves a higher AUROC than the average of ten board-certified breast radiologists (AUROC: 0.962 AI, 0.924 ± 0.02 radiologists). With the help of the AI, radiologists decrease their false positive rates by 37.3% and reduce requested biopsies by 27.8%, while maintaining the same level of sensitivity. This highlights the potential of AI in improving the accuracy, consistency, and efficiency of breast ultrasound diagnosis. Ultrasound is an important imaging modality for the detection and characterization of breast cancer, but it has been noted to have high false-positive rates. Here, the authors present an artificial intelligence system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound imaging.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-021-26023-2