Accuracy of breast ultrasound image analysis software in feature analysis: a comparative study with sonographers

Breast ultrasound is recommended for early breast cancer detection in China, but the rapid increase in imaging data burdens sonographers. This study evaluated the agreement between artificial intelligence (AI) software and sonographers in analyzing breast nodule features. Breast ultrasound images from two hospitals in Shanghai were analyzed by both the software and the sonographers for features including echotexture, echo pattern, orientation, shape, margin, calcification, and posterior echo attenuation. Agreement between software and sonographers was compared using the proportion of agreement and Kappa, with analysis time also evaluated. A total of 493 images were analyzed. The proportion of agreement between software and sonographers in assessing features was 80.5% for echotexture, 84.4% for echo pattern, 93.7% for orientation, 85.8% for shape, 88.6% for margin, 80.5% for calcification, and 90.5% for posterior echo attenuation, highlighting software's high accuracy. Cohen's kappa for other features indicated moderate to substantial agreement (0.411-0.674), with calcification showing fair agreement (0.335). The software significantly reduced analysis time compared to sonographers (P < 0.001). The software showed high accuracy and time efficiency. AI software presents a viable solution for reducing sonographers' workload and enhance healthcare in underserved areas by automating feature analysis in breast ultrasound images.