Medicine and Health

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

Y. Shen, F. E. Shamout, et al.

Discover how a groundbreaking AI system developed by Yiqiu Shen and colleagues has achieved radiologist-level accuracy in detecting breast cancer through ultrasound images, significantly reducing false positives and unnecessary biopsies while enhancing diagnostic sensitivity.

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Index

Abstract

Breast ultrasound, while effective in detecting mammographically occult cancers, suffers from high false-positive rates. This study introduces an AI system achieving radiologist-level accuracy in identifying breast cancer in ultrasound images. Trained on a vast dataset (288,767 exams, 5,442,907 images), the AI boasts an AUROC of 0.976, surpassing the average AUROC of ten board-certified radiologists (0.924). The AI, when used with radiologists, reduced false-positive rates by 37.3% and biopsies by 27.8% while maintaining sensitivity, demonstrating its potential to enhance breast ultrasound diagnosis.

Publisher

Nature Communications

Published On

Oct 26, 2021

Authors

Yiqiu Shen, Farah E Shamout, Jamie R Oliver, Jan Witowski, Kawshik Kannan, Jungkyu Park, Nan Wu, Connor Huddleston, Stacey Wolfson, Alexandra Millett, Robin Ehrenpreis, Divya Awal, Cathy Tyma, Naziya Samreen, Yiming Gao, Chloe Chhor, Stacey Gandhi, Cindy Lee, Sheila Kumari Subaiya, Cindy Leonard, Reyhan Mohammed, Christopher Moczulsk, Jaime Altabe, James Babb, Alana Lewin, Beatriu Reig, Linda Moy, Laura Heacock, Krzysztof J Geras

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