Prediction of visual impairment in retinitis pigmentosa using deep learning and multimodal fundus images.

Journal: The British journal of ophthalmology
PMID: 35896367

Abstract

BACKGROUND: The efficiency of clinical trials for retinitis pigmentosa (RP) treatment is limited by the screening burden and lack of reliable surrogate markers for functional end points. Automated methods to determine visual acuity (VA) may help address these challenges. We aimed to determine if VA could be estimated using confocal scanning laser ophthalmoscopy (cSLO) imaging and deep learning (DL).

Authors

  • Tin Yan Alvin Liu
    Wilmer Eye Institute, Johns Hopkins Hospital, Baltimore, Maryland, USA.
  • Carlthan Ling
    Department of Ophthalmology, University of Maryland Medical System, Baltimore, Maryland, USA.
  • Leo Hahn
    Department of Ophthalmology, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands.
  • Craig K Jones
    2Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore; and.
  • Camiel Jf Boon
    Department of Ophthalmology, Amsterdam UMC Locatie AMC, Amsterdam, The Netherlands.
  • Mandeep S Singh
    Wilmer Eye Institute, Johns Hopkins Hospital, Baltimore, Maryland, USA singhcorrespauth@gmail.com.

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