Chotcomwongse Peranut, Ruamviboonsuk Paisan, Karavapitayakul Chaiwat, Thongthong Koblarp, Amornpetchsathaporn Anyarak, Chainakul Methaphon, Triprachanath Malee, Lerdpanyawattananukul Eckachai, Arjkongharn Niracha, Ruamviboonsuk Varis, Vongsa Nattaporn, Pakaymaskul Pawin, Waiwaree Turean, Ruampunpong Hathaiphan, Tiwari Richa, Tangcharoensathien Viroj
Department of Ophthalmology, College of Medicine, Rajavithi Hospital, Rangsit University, Bangkok, Thailand.
Department of Medical Services (DMS), Ministry of Public Health, Nonthaburi, Thailand.
Ophthalmol Ther. 2025 Feb;14(2):447-460. doi: 10.1007/s40123-024-01086-8. Epub 2025 Jan 10.
INTRODUCTION: Screening diabetic retinopathy (DR) for timely management can reduce global blindness. Many existing DR screening programs worldwide are non-digital, standalone, and deployed with grading retinal photographs by trained personnel. To integrate the screening programs, with or without artificial intelligence (AI), into hospital information systems to improve their effectiveness, the non-digital workflow must be transformed into digital. We developed a cloud-based digital platform and implemented it in an existing DR screening program. METHODS: We conducted the following processes in the platform for prospective DR screening at a community hospital: capturing patients' retinal photographs, uploading them for grading by AI or trained personnel on alternate weeks for 32 weeks, and referring vision-threatening DR to a referral center. At this center, the platform was applied for the assessment of potential missed referrals via remote over-reading by a retinal specialist and tracking referrals. Implementational outcomes, such as detecting positive cases, agreement between AI and over-reading, and referral adherence were assessed. RESULTS: Of 645 patients screened by AI, 201 (31.2%) were referrals, 129 (64.2%) of which were true positives agreeable by over-reading; 115 of these true positives (89.1%) had referral adherence. False negatives judged by over-reading were 1.1% (5/444). Of 730 patients in manual screening, 175 (24.0%) were potential referrals, 11 (6.3%) of which were referred at the point-of-screening; eight of these (72.7%) adhered to referral. The remaining 164 cases were appointed for later examination by a visiting general ophthalmologist; 11 of these 116 examined (9.5%) were referred for non-DR-related eye conditions with 81.8% (9/11) referral adherence. No system failure or interruption was found. CONCLUSIONS: The digital platform can be practically integrated into the existing non-digital DR screening programs to implement AI and monitor previously unknown but important indicators, such as referral adherence, to improve the effectiveness of the programs. TRIAL REGISTRATION: ClinicalTrials.gov. (registration number: NCT05166122).
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