Comparing IOL refraction prediction accuracy and A-constant optimization for cataract surgery patients across South Indian and Midwestern United States populations.

Journal: BMC ophthalmology
Published Date: Jul 2, 2025

Abstract

BACKGROUND: IOL power selection is a key determinant of refractive outcomes after cataract surgery. Numerous formulas exist to aid in this process; some are derived from geometric-optical principles (e.g., SRK/T, Barrett) while others are based on data-driven and machine learning approaches (e.g., Nallasamy, Pearl-DGS). Given differences in ocular biometry and environmental stimuli, population-specific factors may impact the generalizability of certain formulas. This study compares clinical and biometric characteristics and evaluates the prediction accuracy of seven IOL power formulas, including machine learning–based approaches, in two distinct cataract surgery populations from South India and the Midwestern United States.

Authors

  • Omer Siddiqui
    Department of Computational Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Avenue, Ann Arbor, MI, 48109, USA.
  • Elisa Warner
    Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
  • Miles Greenwald
    Department of Ophthalmology and Visual Sciences, Kellogg Eye Center, University of Michigan, 1000 Wall St, Ann Arbor, MI, 48105, USA.
  • Tingyang Li
    Wuhan Business University, Wuhan, Hubei 430000, China.
  • Karthik Srinivasan
    McGovern Institute for Brain Research, Massachusetts Institute of Technology, 43 Vassar Street, Cambridge, Massachusetts 02139, USA.
  • Aravind Haripriya
    Cataract and IOL Services, Aravind Eye Hospital, Chennai, India.
  • Nambi Nallasamy
    Department of Ophthalmology and Visual Sciences, School of Medicine, University of Michigan, Ann Arbor, MI.

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