Application of Machine Learning Based on Ultrasonic RF Time Series and 2D Image Features in the Diagnosis of BI ‐ RADS 4A Lesions of Breast

Objectives This study aimed to develop and validate machine‐learning (ML) models that integrate ultrasonic radiofrequency (RF) time‐series signals with gray‐scale image features for the preoperative differentiation of breast lesions classified as category 4A of the Breast Imaging Reporting and Data System. Methods A dataset comprising RF signals, 2D ultrasound features, and pathological diagnoses from 130 BI‐RADS 4A lesions (128 patients) was analyzed. Five ML models (logistic regression [LR], support vector machine [SVM], k‐nearest neighbor [k‐NN], and gradient boosting [GB]) were evaluated. Results Among 31 features (28 RF‐derived and 5 2D image features), 6 key features were selected through feature selection. The LR model achieved the highest area under the curve (0.81, 95% confidence interval: 0.66–1.00), though no statistically significant differences were observed among models (DeLong test, p > .05). Artificial intelligence‐assisted diagnosis improved accuracy across physician seniority levels ( p < .05): junior (≤3 years: 52.28% versus baseline 27.28%), intermediate (4–10 years: 79.54% versus 45.46%), and senior (≥10 years: 81.91% versus 63.63%). Conclusion The integration of RF time series and 2D features via LR demonstrates potential to reduce unnecessary biopsies by enhancing diagnostic precision, particularly for less experienced clinicians.