Application of an artificial intelligence-assisted diagnostic system for breast ultrasound: a prospective study

Accurate diagnosis of breast cancer is of great importance to improve the prognosis of patients. Artificial intelligence (AI)-assisted diagnostic system for breast ultrasound is gradually being applied in the identification of benign and malignant breast lesions. This study aimed to evaluate the diagnostic performance and optimal application of AI-assisted ultrasonography for breast lesions in clinical setting.

A total of 501 consecutive patients with 679 breast lesions were prospectively included in the study. Junior and senior radiologists were asked to interpret images of lesions with and without AI assistance, respectively. Three application modes of AI were employed: AI alone, adjusted Breast Imaging Reporting and Data System (BI-RADS; incorporating BI-RADS obtained by AI into BI-RADS obtained by radiologists), and second reading mode (combining characteristic information extracted by AI to conduct a second reading so as to obtain a new BI-RADS). The diagnostic performances of these application modes were analyzed and compared.

The area under the curve (AUC) of junior radiologists increased from 0.879 to 0.921 in BI-RADS_{second reading}, which was higher than that in BI-RADS_adjusted (0.901), similar to that in AI alone (0.924), and lower than that obtained by senior radiologists (0.950). Using BI-RADS category 4A as the threshold, the sensitivity of junior radiologists was found to increase from 0.83 to 0.92 (P<0.001). Furthermore, the specificity increased from 0.79 to 0.85, which was higher than those of AI alone and BI-RADS_adjusted (P<0.001). The unnecessary biopsy rate decreased by 14.70% (P=0.01). For senior radiologists, the sensitivity increased from 0.91 to 0.96 (P=0.01). Similar results were observed in the subgroup analysis of lesions ≤2 cm. For lesions >2 cm, only the specificity of junior radiologists increased from 0.39 to 0.52 (P=0.03).

AI-assisted ultrasound is useful for the diagnosis of breast lesions, particularly for junior radiologists and lesions ≤2 cm. The use of the second reading mode can achieve excellent diagnostic performance.