Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI

Presentation Time: 08:45 AM - 09:00 AM

Abstract Keywords: Fairness and Elimination of Bias, Machine Learning, Imaging Informatics
Primary Track: Applications
Programmatic Theme: Translational Bioinformatics

Machine learning (ML) algorithms play a crucial role in the early and accurate diagnosis of Alzheimer's Disease (AD), which is essential for effective treatment planning. However, existing methods are not well-suited for identifying Mild Cognitive Impairment (MCI), a critical transitional stage between normal aging and AD. This inadequacy is primarily due to label imbalance and bias from different sensitve attributes in MCI classification. To overcome these challenges, we have designed an end-to-end fairness-aware approach for label-imbalanced classification, tailored specifically for neuroimaging data. This method, built on the recently developed FACIMS framework, integrates into STREAMLINE, an automated ML environment. We evaluated our approach against nine other ML algorithms and found that it achieves comparable balanced accuracy to other methods while prioritizing fairness in classifications with five different sensitive attributes. This analysis contributes to the development of equitable and reliable ML diagnostics for MCI detection.

Speaker(s):
Boning Tong
University of Pennsylvania

Author(s):
Boning Tong - University of Pennsylvania; Travyse Edwards, BA - University of Pennsylvania; Shu Yang; Bojian Hou, PhD - University of Pennsylvania; Davoud Tarzanagh, PhD - University of Pennsylvania; Ryan Urbanowicz, PhD - Cedars-Sinai Medical Center; Jason Moore, PhD, FACMI - Cedars-Sinai; Marylyn Ritchie, PhD - University of Pennsylvania, Perelman School of Medicine; Christos Davatzikos, PhD - University of Pennsylvania; Li Shen, Ph.D. - University of Pennsylvania;