人工智能
深度学习
机器学习
医学
队列
接收机工作特性
乳腺超声检查
乳房成像
乳腺癌
置信区间
医学物理学
超声波
工作流程
计算机科学
放射科
癌症
乳腺摄影术
病理
内科学
数据库
作者
Yan Lin,Zhiying Liang,Hao Zhang,Gaosong Zhang,Zheng Wei-wei,Jing Pei,Dongsheng Yu,Hanqi Zhang,Xinxin Xie,Chang Liu,Wenxin Zhang,Hui Zheng,Jing Pei,Dinggang Shen,Xuejun Qian
标识
DOI:10.1038/s43856-024-00518-7
摘要
Abstract Background Though deep learning has consistently demonstrated advantages in the automatic interpretation of breast ultrasound images, its black-box nature hinders potential interactions with radiologists, posing obstacles for clinical deployment. Methods We proposed a domain knowledge-based interpretable deep learning system for improving breast cancer risk prediction via paired multimodal ultrasound images. The deep learning system was developed on 4320 multimodal breast ultrasound images of 1440 biopsy-confirmed lesions from 1348 prospectively enrolled patients across two hospitals between August 2019 and December 2022. The lesions were allocated to 70% training cohort, 10% validation cohort, and 20% test cohort based on case recruitment date. Results Here, we show that the interpretable deep learning system can predict breast cancer risk as accurately as experienced radiologists, with an area under the receiver operating characteristic curve of 0.902 (95% confidence interval = 0.882 – 0.921), sensitivity of 75.2%, and specificity of 91.8% on the test cohort. With the aid of the deep learning system, particularly its inherent explainable features, junior radiologists tend to achieve better clinical outcomes, while senior radiologists experience increased confidence levels. Multimodal ultrasound images augmented with domain knowledge-based reasoning cues enable an effective human-machine collaboration at a high level of prediction performance. Conclusions Such a clinically applicable deep learning system may be incorporated into future breast cancer screening and support assisted or second-read workflows.
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