超音波検査による乳房結節の特性評価における人工知能プログラムの検証

Breast ultrasound is a widely accessible imaging method but highly operator-dependent. Artificial intelligence (AI) may improve breast lesion characterization, aiding in diagnostic decisions.

To validate an AI system (Koios DS v3.1) in the BI-RADS classification of breast lesions on ultrasound.

This cross-sectional diagnostic study included 100 women with breast lesions on ultrasound (July 2022-July 2023), later submitted to histopathology. BI-RADS classifications by conventional ultrasound were compared with AI-based classifications and histopathological findings. Diagnostic agreement and validity measures were calculated.

The median patient age was 58.5 years (range, 31-89). AI identified lesions in 93 % of cases. Moderate agreement (Kappa 0.41-0.60) was found between AI and conventional ultrasound BIRADS classification (Kappa = 0.405). When compared with histopathology, AI showed a Kappa of 0.626, with 95.6 % sensitivity, 68.0 % specificity, and a 2.1 % false-negative rate. Disagreement was significantly higher for lesions situated in the lower or central quadrants of the breast (OR = 4.55; p = 0.009) and in irregular heterogeneous areas (OR = 8.27; p < 0.001).

AI demonstrated high sensitivity and low false-negative rates in classifying breast lesions by ultrasound, showing potential as a complementary diagnostic tool. However, limitations persist, especially in irregular heterogeneous areas and for lesions situated in the lower or central quadrants of the breast.