Reducing Ophthalmic Health Disparities Through Transfer Learning: A Novel Application to Overcome Data Inequality.

Journal: Translational vision science & technology
PMID: 38038606

Abstract

PURPOSE: Race disparities in the healthcare system and the resulting inequality in clinical data among different races hinder the ability to generate equitable prediction results. This study aims to reduce healthcare disparities arising from data imbalance by leveraging advanced transfer learning (TL) methods.

Authors

  • TingFang Lee
    Department of Ophthalmology, NYU Langone Health, New York, NY, USA.
  • Gadi Wollstein
    Department of Ophthalmology, NYU Langone Health, NYU Eye Center, New York, New York.
  • Chisom T Madu
    Department of Ophthalmology, NYU Langone Health, New York, NY, USA.
  • Andrew Wronka
    Department of Ophthalmology, NYU Langone Health, New York, NY, USA.
  • Lei Zheng
    Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin's Clinical Research Center for Cancer, Huanhuxi Road, Hexi District, Tianjin 300060, China.
  • Ronald Zambrano
    Department of Ophthalmology, NYU Langone Health, New York, NY, USA.
  • Joel S Schuman
    Department of Ophthalmology, NYU Langone Health, NYU Eye Center, New York, New York.
  • Jiyuan Hu
    Departments of Population Health, NYU Langone Health, New York, NY, USA.

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