Ensuring Fairness in Detecting Mild Cognitive Impairment with MRI.

Journal: AMIA ... Annual Symposium proceedings. AMIA Symposium
Published Date: May 22, 2025

Abstract

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.

Authors

  • Boning Tong
    University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Travyse Edwards
    Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA, USA.
  • Shu Yang
    Department of Health Management, Bengbu Medical College, Bengbu, 233030.
  • Bojian Hou
    University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Davoud Ataee Tarzanagh
    University of Pennsylvania, Philadelphia, PA 19104, USA.
  • Ryan J Urbanowicz
    Clinical Research Informatics Core, Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, PA, USA.
  • Jason H Moore
    University of Pennsylvania, Philadelphia, PA, USA.
  • Marylyn D Ritchie
    From the Departments of Bioengineering (M.S.Y.), Radiology (H.S., N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K., W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd, Philadelphia, PA 19104; Department of Radiology, Loyola University Medical Center, Maywood, Ill (A.D.G.); Department of Information Services, University of Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health Economics, University of Pennsylvania, Philadelphia, Pa (A.B.).
  • Christos Davatzikos
    Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL), Center for and Data Science for Integrated Diagnostics (AID), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Li Shen
    Department of Clinical Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.

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