Prospective pragmatic trial of automated retinal photography and AI glaucoma screening in Australian primary care
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 A...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01768-y |
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| author | Catherine L. Jan Sanil Joseph Algis J. Vingrys Jacqueline Henwood Zongyuan Ge Randall S. Stafford Mingguang He |
| author_facet | Catherine L. Jan Sanil Joseph Algis J. Vingrys Jacqueline Henwood Zongyuan Ge Randall S. Stafford Mingguang He |
| author_sort | Catherine L. Jan |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-01415a8fb563414ab0fb17efe0d2debe |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-01415a8fb563414ab0fb17efe0d2debe2025-08-20T04:01:36ZengNature Portfolionpj Digital Medicine2398-63522025-07-01811810.1038/s41746-025-01768-yProspective pragmatic trial of automated retinal photography and AI glaucoma screening in Australian primary careCatherine L. Jan0Sanil Joseph1Algis J. Vingrys2Jacqueline Henwood3Zongyuan Ge4Randall S. Stafford5Mingguang He6Centre for Eye Research Australia, Royal Victorian Eye and Ear HospitalCentre for Eye Research Australia, Royal Victorian Eye and Ear HospitalDepartment of Optometry and Vision Sciences, The University of MelbourneCentre for Eye Research Australia, Royal Victorian Eye and Ear HospitalThe AIM for Health Lab, Faculty of IT, Monash UniversityStanford Prevention Research Center, Stanford University School of MedicineCentre for Eye Research Australia, Royal Victorian Eye and Ear HospitalAbstract 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.https://doi.org/10.1038/s41746-025-01768-y |
| spellingShingle | Catherine L. Jan Sanil Joseph Algis J. Vingrys Jacqueline Henwood Zongyuan Ge Randall S. Stafford Mingguang He Prospective pragmatic trial of automated retinal photography and AI glaucoma screening in Australian primary care npj Digital Medicine |
| title | Prospective pragmatic trial of automated retinal photography and AI glaucoma screening in Australian primary care |
| title_full | Prospective pragmatic trial of automated retinal photography and AI glaucoma screening in Australian primary care |
| title_fullStr | Prospective pragmatic trial of automated retinal photography and AI glaucoma screening in Australian primary care |
| title_full_unstemmed | Prospective pragmatic trial of automated retinal photography and AI glaucoma screening in Australian primary care |
| title_short | Prospective pragmatic trial of automated retinal photography and AI glaucoma screening in Australian primary care |
| title_sort | prospective pragmatic trial of automated retinal photography and ai glaucoma screening in australian primary care |
| url | https://doi.org/10.1038/s41746-025-01768-y |
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