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

Yiqiu Shen, Farah E. Shamout, Jamie R. Oliver, Jan Witowski, Kawshik Kannan, Jungkyu Park, Nan Wu, Connor Huddleston, Stacey Wolfson, Alexandra Millet, Robin Ehrenpreis, Divya Awal, Cathy Tyma, Naziya Samreen, Yiming Gao, Chloe Chhor, Stacey Gandhi, Cindy Lee, Sheila Kumari-Subaiya, Cindy Leonard, Reyhan Mohammed, Christopher Moczulski, Jaime Altabet, James Babb, Alana Lewin, Beatriu Reig, Linda Moy, Laura Heacock and Krzysztof J. Geras ()
Additional contact information
Yiqiu Shen: New York University
Farah E. Shamout: NYU Abu Dhabi
Jamie R. Oliver: NYU Grossman School of Medicine
Jan Witowski: NYU Grossman School of Medicine
Kawshik Kannan: Courant Institute, New York University
Jungkyu Park: NYU Grossman School of Medicine
Nan Wu: New York University
Connor Huddleston: NYU Grossman School of Medicine
Stacey Wolfson: NYU Grossman School of Medicine
Alexandra Millet: NYU Grossman School of Medicine
Robin Ehrenpreis: NYU Grossman School of Medicine
Divya Awal: NYU Grossman School of Medicine
Cathy Tyma: NYU Grossman School of Medicine
Naziya Samreen: NYU Grossman School of Medicine
Yiming Gao: NYU Grossman School of Medicine
Chloe Chhor: NYU Grossman School of Medicine
Stacey Gandhi: NYU Grossman School of Medicine
Cindy Lee: NYU Grossman School of Medicine
Sheila Kumari-Subaiya: NYU Grossman School of Medicine
Cindy Leonard: NYU Grossman School of Medicine
Reyhan Mohammed: NYU Grossman School of Medicine
Christopher Moczulski: NYU Grossman School of Medicine
Jaime Altabet: NYU Grossman School of Medicine
James Babb: NYU Grossman School of Medicine
Alana Lewin: NYU Grossman School of Medicine
Beatriu Reig: NYU Grossman School of Medicine
Linda Moy: NYU Grossman School of Medicine
Laura Heacock: NYU Grossman School of Medicine
Krzysztof J. Geras: New York University

Nature Communications, 2021, vol. 12, issue 1, 1-13

Abstract: Abstract 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.

Date: 2021
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DOI: 10.1038/s41467-021-26023-2

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