Advancing fair and explainable machine learning for neuroimaging dementia pattern classification in multi-racial and multi-ethnic populations

Abstract

Abstract Dementia, a degenerative disease affecting millions globally, is projected to triple by 2050. Early and precise diagnosis is essential for effective treatment and improved quality of life. However, current diagnostic approaches often show inconsistent performance across multi-racial and multi-ethnic groups, raising concerns about fairness and clinical reliability. This study investigates performance discrepancies in dementia classification among 6584 Non-Hispanic White, 1263 Non-Hispanic African American, and 713 Hispanic White populations. We observed significant cross-group bias, particularly when models trained on one group are tested on another. To address this, we evaluated RegAlign, a few-shot domain adaptation objective that combines source-side focal learning, target-side class-weighted supervision, and class-conditional alignment to improve adaptation to underrepresented populations. Our results show that this approach substantially reduces inter-group performance gaps, especially between Non-Hispanic White and Hispanic populations. Here, we show the importance of fairness-aware learning strategies and diverse training data for improving the accuracy and equity of MRI-based dementia classification.