AI Can Improve Breast Diagnosis – Consumer Health News

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AI Can Improve Breast Diagnosis - Consumer Health News

The researchers found that the AI system achieved an area under the receiver operating characteristic curve (AUROC) of 0.976 on a test set consisting of 44,755 exams. The AI system achieved a higher AUROC than the average of 10 board-certified breast radiologists in a retrospective reader study (AUROC, 0.962 for AI; 0.924 for radiologists). Radiologists decreased their false-positive rates by 37.3 percent and reduced the number of requested biopsies by 27.8 percent with the help of AI; the same level of sensitivity was maintained. The system was evaluated on an independent external test dataset to confirm its generalizability and achieved an AUROC of 0.927.

Abstract/Full Text

Story Highlights

  • Yiqiu Shen, from New York University in New York City, and colleagues curated a dataset consisting of 288,767 ultrasound exams from 143,203 patients examined at NYU Langone Health between 2012 and 2019 to develop and validate an AI system that achieves radiologist-level accuracy in identifying breast cancer in ultrasound images.

  • “If our efforts to use machine learning as a triaging tool for ultrasound studies prove successful, ultrasound could become a more effective tool in breast cancer screening, especially as an alternative to mammography, and for those with dense breast tissue,” a coauthor said in a statement. “Its future impact on improving women’s breast health could be profound.”