Prospective pragmatic trial of automated retinal photography and AI glaucoma screening in Australian primary care.

Journal: NPJ digital medicine
Published Date: Jul 1, 2025

Abstract

There are no prospective clinical studies evaluating artificial intelligence implementation for glaucoma detection in real-world settings. We developed an automated retinal photography and AI-based screening system and prospectively assessed its accuracy, feasibility, and acceptability in Australian general practice (GP) clinics. Adults aged 50 years or older were recruited during routine GP visits, with retinal images captured using an automated fundus camera and analysed by the AI system for glaucoma risk classification. Of 414 participants, 277 (66.9%) had analysable images, with a total of 483 eyes included. The AI system achieved an AUROC of 0.80, sensitivity of 65.0%, and specificity of 94.6%. Among 161 previously undiagnosed patients, 18 (11.2%) were identified as referable glaucoma. Patient feedback was positive, and clinic staff supported AI-assisted screening to enhance glaucoma care. Despite challenges such as lower sensitivity and image acquisition limitations, the system shows promise for opportunistic screening in primary care settings.

Authors

  • Catherine L Jan
    Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia. catherine.jan@student.unimelb.edu.au.
  • Sanil Joseph
    From the Centre for Eye Research Australia (S.J, X.S, M.H), Royal Victorian Eye and Ear Hospital, East Melbourne, Australia; Department of Surgery (Ophthalmology) (S.J, X.S, M.H), The University of Melbourne, Melbourne, Australia; Lions Aravind Institute of Community Ophthalmology (S.J, J.S, T.R), Aravind Eye Care System. Madurai, India. Electronic address: sanil@aravind.org.
  • Algis J Vingrys
    Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, VIC, Australia.
  • Jacqueline Henwood
    Centre for Eye Research Australia, University of Melbourne, Melbourne, Australia.
  • Zongyuan Ge
    AIM for Health Lab, Faculty of IT, Monash University, Clayton, Victoria, Australia; Monash-Airdoc Research Lab, Faculty of IT, Monash University, Clayton, Victoria, Australia.
  • Randall S Stafford
    Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, USA.
  • Mingguang He
    State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou 510060, China; Centre for Eye Research Australia; Departments of Ophthalmology and Surgery, University of Melbourne, Melbourne, Australia. Electronic address: mingguang.he@unimelb.edu.au.

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