ScholarWorks@중앙대학교

초록

To assess the diagnostic performance and clinical usefulness of deep learning-based computer-aided detection (AI-CAD) for automated breast ultrasound (ABUS) across radiologists with varying ABUS experience. This retrospective study included 114 women (228 breasts) who underwent ABUS in 2019. Three radiologists interpreted images with and without AI-CAD. We evaluated sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, and area under the curve (AUC), reading time and interobserver agreement in Breast Imaging Reporting & Data System (BI-RADS) categorization and biopsy recommendations. Among 114 women (50.9 ± 10.8 years), 28 were diagnosed with breast cancer. The following performance metrics improved significant with AI-CAD: Reader 1 (least experienced of ABUS; 2 years of ABUS experience), AUC, 0.837 to 0.947 (p = 0.009), and NPV, 95.8% to 98.4% (p = 0.022); Reader 2 (7 years of experience), PPV, 50.0% to 59.5% (p = 0.042); Reader 3 (8 years of experience), PPV, 55.6% to 66.7% (p = 0.034). Reader 1 with AI-CAD achieved a performance comparable or higher than those of more experienced readers without AI. Specifically: compared with Reader 2, specificity (93.5% vs. 88.0%), PPV (65.8% vs. 50.0%), and accuracy (93.0% vs. 87.7%) were higher. Although Reader 3 originally demonstrated higher NPV (98.4% vs. 95.8%) and AUC (0.954 vs. 0.837) without CAD, these differences were no longer significant when Reader 1 used AI-CAD. Across all readers, AI-CAD reduced the mean reading time by an average of 25 s (p < 0.001). Inter-observer agreement after AI-CAD use (BI-RADS κ: 0.279 → 0.363; biopsy recommendation κ: 0.666 → 0.736) showed no statistically significant difference. AI-CAD enhanced diagnostic performance and reading efficiency in ABUS interpretation, demonstrating the most pronounced improvement for the less experienced reader.

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