AI-Assisted Decision-Support Framework for Breast Density and Background Parenchymal Enhancement Assessment in Contrast-Enhanced Mammography.
AI decision support associated with 26% less disagreement in breast density and enhancement categorization
AI-Assisted Decision-Support Framework for Breast Density and Background Parenchymal Enhancement Assessment in Contrast-Enhanced Mammography.
Interobserver variability in breast density and Background Parenchymal Enhancement (Background Parenchymal Enhancement) assessment remains a major limitation in Contrast- Enhanced Mammography (Contrast- Enhanced Mammography) reporting consistency.
Building on the BPE-CEM Standard Scale (BPE-CEM Standard Scale) framework introduced in Part 1, this study aimed to evaluate whether a structured artificial intelligence-assisted decision-support model based on expert-derived variables could improve consistency in BPE-CEM Standard Scale-related interpretation, particularly in disagreement-prone dense breast categories, rather than function as an autonomous image-based grading system.
We retrospectively analyzed 213 consecutive Contrast- Enhanced Mammography examinations with BI-RADS 4-5 lesions and histologically confirmed malignancy.
Overall performance supported feasibility within a decision-support context (AUC 0.75; precision 0.72; recall 0.69).
Findings emphasize interpretative support rather than diagnostic automation.
A structured AI-assisted predictive framework showed preliminary potential to enhance consistency of BPE-Contrast- Enhanced Mammography Standard Scale interpretation in Contrast- Enhanced Mammography, particularly in dense breasts, while requiring prospective multicenter validation before broader clinical adoption.
These results support the role of transparent decision-support tools aimed at reducing observer variability while maintaining radiologist oversight.