Dynamic AI-assisted Ipsilateral Tissue Matching for Digital Breast Tomosynthesis

To compare Digital Breast Tomosynthesis (DBT) tissue matching errors with and without artificial intelligence (AI) assistance to typical screen-detected breast tumor sizes, evaluating whether AI ameliorates lesion mislocalization beyond tumor boundaries, especially for nonexpert radiologists. The technology category is deep learning. This multicenter retrospective feasibility study conducted in April 2022 – July 2023 included 12 radiologists (mean age, 42 years ± 8) interpreting 94 lesion regions of interest in 30 women. Readers performed annotations with and without AI assistance after a minimum four-week washout period. The root mean square errors (RMSE) and maximum distance errors (MDE) were measured relative to consensus references. Stratifications included radiologist expertise (≥ 5 vs < 5 years), lesion abnormal-ity, and AI warnings. The Wilcoxon signed-rank test was used to assess statistical significance. Across all abnormal lesions, mean RMSE was 32% higher without AI (11.70mm vs 8.88mm, p = .049), and mean maximum distance errors were 37.5% higher (20.68mm vs 15.08mm, p = .036). Non-expert radiologists showed the largest benefit: for abnormal lesions without AI warnings, RMSE was 61.9% higher without AI (12.20mm vs 7.57mm, p = .010) and maximum distance error was 67.5% higher (15.76mm vs 9.47mm, p = .028). These reductions are clinically relevant given typical screen-detected breast tumor sizes (median, 13mm [IQR: 9–20]). AI-assisted tissue matching significantly reduced DBT localization errors, particularly for non-experts handling challenging cases. By keeping errors below typical tumor dimensions, AI may improve diagnostic precision and reduce risks of missed or mischaracterized lesions. Dynamic artificial intelligence assisted tissue matching in digital breast tomosynthesis improves localization accuracy for non-expert radiologists, with errors in abnormal cases significantly larger (67.5%, p ≤ 0.05) without assistance. In this multicenter retrospective study of 94 Regions of Interest (ROI) analyzed by 12 radiologists across 5 hospitals, manual tissue matching was found to have errors 32% higher in abnormal cases than AI-assisted tissue matching (p < 0.05). For non-expert radiologists interpreting abnormal cases (excluding cases with AI warnings) without AI assistance, the root mean squared distance errors (RMSE) were found to be 61.9% higher (12.20mm vs 7.57mm, p < 0.01) and the maximum distance errors (MDE) was 67.6% higher (15.76mm vs 9.47mm, p < 0.05) than when using AI assistance. For challenging cases, many non-expert readers’ MDE without AI assistance (75th percentile: 20.21mm) exceeded the largest tumor dimensions (75th percentile: 20mm), while AI-assisted errors (75th percentile: 11.94mm, p < 0.05) remained within median tumor sizes (12mm), potentially preventing correlation with non-lesion tissue.