YOLO AI model based on an automated breast volume scanner for the detection of benign and malignant breast lesions

The automated breast volume scanner (ABVS), a type of ultrasound device, plays a crucial role in breast cancer screening; however, the ABVS data volume places a strain on clinicians. We aimed to develop an artificial intelligence (AI) model for the detection and classification of lesions as benign or malignant during ABVS examination.

This retrospective study included 1,284 patients with 1,769 lesions who underwent ABVS examination between January 2017 and August 2021. The lesions were randomly divided into training and test sets at a 7:3 ratio. Using the test set, the performance of the You Only Look Once (YOLO) AI model, based on the YOLO version 8 architecture, in single-target (background vs. lesion), categorical (benign vs. malignant), and varied lesion diameter detection was evaluated. Finally, differences in the diagnoses of four radiologists with different levels of experience before and after receiving AI model assistance were assessed.

The recall of the YOLO AI model for single-target detection was 0.983. The precision, recall, mean average precision (mAP) 50, and F1-score of the YOLO AI model for categorized target detection were 0.887, 0.866, 0.919, and 0.876, respectively. While the precision, recall, mAP50, and F1-score of the YOLO AI model for the classification of lesions with diameters ≤10 mm, 10 mm < diameters ≤ 20 mm, 20 mm < diameters ≤ 30 mm, and diameters >30 mm were 0.910, 0.806, 0.868, 0.855; 0.895, 0.844, 0.911, 0.869; 0.876, 0.867, 0.917, 0.871; and 0.882, 0.898, 0.941, 0.890, respectively. The area under the curve (AUC) values of the radiologists after they received YOLO AI assistance in the diagnosis of breast lesions were 0.806, 0.890, 0.897, and 0.895, respectively, and these AUC values were better than their AUC values before they received YOLO AI assistance (P<0.001).

The YOLO AI model can effectively identify and characterize breast lesions. It improves radiologists' diagnostic performance and bridges expertise gaps between radiologists.