Comparison of diagnostic performance between artificial intelligence-assisted automated breast ultrasound and handheld ultrasound in breast cancer screening.

Objective: To compare the diagnostic performance of artificial intelligence-assisted automated breast ultrasound (AI-ABUS) with traditional handheld ultrasound (HHUS) in breast cancer screening. Methods: A total of 36 171 women undergoing breast cancer ultrasound screening in Futian District, Shenzhen, between July 1, 2023 and June 30, 2024 were prospectively recruited and assigned to either the AI-ABUS or HHUS group based on the screening modality used. In the AI-ABUS group, image acquisition was performed on-site by technicians, and two ultrasound physicians conducted remote diagnoses with AI assistance, supported by a follow-up management system. In the HHUS group, one ultrasound physician conducted both image acquisition and diagnosis on-site, and follow-up was led by clinical physicians. Based on the reported malignancy rates of different BI-RADS categories, the number of undiagnosed breast cancer cases in individuals without pathology was estimated, and adjusted detection rates were calculated. Primary outcomes included screening positive rate, biopsy rate, cancer detection rate, loss-to-follow-up rate, specificity, and sensitivity. Results: The median age interquartile range, M (Q1, Q3) of the 36 171 women was 43.8 (36.6, 50.8) years. A total of 14 766 women (40.82%) were screened with AI-ABUS and 21 405 (59.18%) with HHUS. Baseline characteristics showed no significant differences between the groups (all P>0.05). The AI-ABUS group had a lower screening positive rate 0.59% (87/14 766) vs 1.94% (416/21 405), but higher biopsy rate 47.13% (41/87) vs 16.10% (67/416), higher cancer detection rate 1.69‰ (25/14 766) vs 0.47‰ (10/21 428), and lower loss-to-follow-up rate (6.90% vs 71.39%) compared to the HHUS group (all P0.05). The specificity of AI-ABUS was higher than that of HHUS 89.77% (13, 231/14 739) vs 74.12% (15, 858/21 394), P0.05. After estimating undiagnosed cancer cases among participants without pathology, the adjusted detection rate was 2.30‰ (34/14 766) in the AI-ABUS group and ranged from 1.17‰ to 2.75‰ (25-59)/21 428 in the HHUS group. In the minimum estimation scenario, the detection rate in the AI-ABUS group was significantly higher (P0.05). Conclusions: The AI-ABUS model, combined with an intelligent follow-up management system, enables a higher breast cancer detection rate with a lower screening positive rate, improved specificity, and reduced loss to follow-up. This suggests AI-ABUS is a promising alternative model for breast cancer screening.