Multicenter Validation of Deep Learning Algorithm ROP.AI for the Automated Diagnosis of Plus Disease in ROP.

Journal: Translational vision science & technology
PMID: 37578427

Abstract

PURPOSE: Retinopathy of prematurity (ROP) is a sight-threatening vasoproliferative retinal disease affecting premature infants. The detection of plus disease, a severe form of ROP requiring treatment, remains challenging owing to subjectivity, frequency, and time intensity of retinal examinations. Recent artificial intelligence (AI) algorithms developed to detect plus disease aims to alleviate these challenges; however, they have not been tested against a diverse neonatal population. Our study aims to validate ROP.AI, an AI algorithm developed from a single cohort, against a multicenter Australian cohort to determine its performance in detecting plus disease.

Authors

  • Amelia Bai
    Department of Ophthalmology, Queensland Children's Hospital, South Brisbane, Queensland, Australia.
  • Shuan Dai
    Department of Ophthalmology, Queensland Children's Hospital, South Brisbane, Queensland, Australia.
  • Jacky Hung
    Centre for Children's Health Research, South Brisbane, Queensland, Australia.
  • Aditi Kirpalani
    Department of Ophthalmology, Gold Coast University Hospital, Southport, Queensland, Australia.
  • Heather Russell
    Department of Ophthalmology, Gold Coast University Hospital, Southport, Queensland, Australia.
  • James Elder
    Department of Ophthalmology, Royal Women's Hospital, Parkville, Victoria, Australia.
  • Shaheen Shah
    Mater Misericordiae, South Brisbane, Queensland, Australia.
  • Christopher Carty
    Griffith Centre of Biomedical and Rehabilitation Engineering (GCORE), Menzies Health Institute Queensland, Griffith University, Southport, Australia.
  • Zachary Tan
    Faculty of Medicine, The University of Queensland, Brisbane.

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