Automated AI Pipeline for Interpretable Longitudinal Mammography Analysis
Evaluation of morphologic features in breast tissue and suspicious findings, and estimation of its temporal changes are the concomitant processes in mammogram interpretation for breast cancer screening. Artificial intelligent (AI) tools with the ability to automatically detect temporal changes in breast tissues have the potential to increase the specificity and accuracy of breast cancer detection, which is especially beneficial in lower socioeconomic settings with limited access to well-trained mammogram-specialized radiologists. However, current commercial and research computer-assisted detection techniques mostly focus on interpretation of screening mammography at a single time point and cannot efficiently track temporal changes. Preliminary efforts have been devoted to developing AI models using serial exams to diagnose breast cancer, but these models suffer from a common shortcoming of limited interpretability. These gaps limit the generalizability and clinical utility of existing AI tools for mammogram-based cancer detection. To address the unmet need, we propose to develop an AI-based cancer detection pipeline to automatize longitudinal mammogram analysis by integrating multi-modal screening information and demonstrating interpretable characterization of temporal changes in breast tissue and findings.