Validation of a Visual Field Prediction Tool for Glaucoma: A Multicenter Study Involving Patients With Glaucoma in the United Kingdom.

Journal: American journal of ophthalmology
PMID: 39814092

Abstract

PURPOSE: A previously developed machine-learning approach with Kalman filtering technology accurately predicted the disease trajectory for patients with various glaucoma types and severities using clinical trial data. This study assesses performance of the KF approach with real-world data.

Authors

  • Arlen Dean
    From the Department of Industrial and Operations Engineering (A.D., M.P.V.O., M.S.L.), University of Michigan College of Engineering, Ann Arbor, Michigan, USA.
  • Dun Jack Fu
    Moorfields Eye Hospital, London, United Kingdom.
  • Mohammad Zhalechian
    Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan.
  • Mark P Van Oyen
    Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan.
  • Mariel S Lavieri
    Department of Industrial and Operations Engineering, University of Michigan College of Engineering, Ann Arbor, Michigan.
  • Anthony P Khawaja
    NIHR Biomedical Research Centre at Moorfields Eye Hospital and UCL Institute of Ophthalmology, London EC1V 9EL, UK; MRC Epidemiology Unit, University of Cambridge, Cambridge CB2 0SL, UK.
  • Joshua D Stein
    Department of Ophthalmology & Visual Sciences, University of Michigan Kellogg Eye Center, Ann Arbor, Michigan.

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