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. 2025 Feb;14(2):447-460.
doi: 10.1007/s40123-024-01086-8. Epub 2025 Jan 10.

Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research

Peranut Chotcomwongse  1 , Paisan Ruamviboonsuk  2 , Chaiwat Karavapitayakul  3 , Koblarp Thongthong  4 , Anyarak Amornpetchsathaporn  1 , Methaphon Chainakul  1 , Malee Triprachanath  5 , Eckachai Lerdpanyawattananukul  5 , Niracha Arjkongharn  1 , Varis Ruamviboonsuk  6 , Nattaporn Vongsa  1 , Pawin Pakaymaskul  1 , Turean Waiwaree  4 , Hathaiphan Ruampunpong  7 , Richa Tiwari  7 , Viroj Tangcharoensathien  8
Affiliations

Transforming Non-Digital, Clinical Workflows to Detect and Track Vision-Threatening Diabetic Retinopathy via a Digital Platform Integrating Artificial Intelligence: Implementation Research

Peranut Chotcomwongse et al. Ophthalmol Ther. 2025 Feb.

Abstract

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).

Keywords: Artificial intelligence; Diabetic retinopathy; Digital health; Real-world implementation; Vision-threatening diabetic retinopathy.

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Conflict of interest statement

Declarations. Conflict of Interest: Paisan Ruamviboonsuk received consulting fees from and participated in Advisory Boards of Roche and Bayer; honoraria for lectures for Roche, Novartis, and Bayer; support for meeting or traveling from Roche and Bayer; he is a member of the Editorial Board of Ophthalmology and Therapy but was not involved in the selection of peer reviewers for this manuscript or in any subsequent editorial decisions. Richa Tiwari is a Google employee and owns stock at Alphabet. Hathaiphan Ruampunpong is an employee of Persolkelly HR services recruitment subcontracted with Google. The remaining authors (Peranut Chotcomwongse, Chaiwat Karavapitayakul, Koblarp Thongthong, Anyarak Amornpetchsathaporn, Methaphon Chainakul, Malee Triprachanath, Eckachai Lerdpanyawattananukul, Niracha Arjkongharn, Varis Ruamviboonsuk, Nattaporn Vongsa, Pawin Pakaymaskul, Turean Waiwaree and Viroj Tangcharoensathien) declare no competing interest. Ethical Approval: This study was approved by the Research Ethics Committee of Rajavithi Hospital (reference number: 64077) and fully adhered to Thailand’s Personal Data Protection Act 2019. All patient data were de-identified; all patients signed informed consent forms.

Figures

Fig. 1
Fig. 1
Screening workflow of each modality. AI artificial intelligence, VA visual acuity; +  = screened positive for referral; – = screened negative for referral; TP true positive, FP false positive, FN false negative, TN true negative, RTC return to the monthly ophthalmologist clinic. The number in each box indicates the number of patients
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References

    1. Wong TY, Sabanayagam C. Strategies to tackle the global burden of diabetic retinopathy: from epidemiology to artificial intelligence. Ophthalmologica. 2020;243(1):9–20. - PubMed
    1. Steinmetz JD, Bourne RRA, Briant PS, Flaxman SR, Taylor HRB, Jonas JB, et al. Causes of blindness and vision impairment in 2020 and trends over 30 years, and prevalence of avoidable blindness in relation to VISION 2020: the right to sight: an analysis for the Global Burden of Disease Study. Lancet Glob Health. 2021;9(2):e144–60. - PMC - PubMed
    1. Wong TY, Sun J, Kawasaki R, Ruamviboonsuk P, Gupta N, Lansingh VC, et al. Guidelines on diabetic eye care: the international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings. Ophthalmology. 2018;125(10):1608–22. - PubMed
    1. World Health Organization. Regional Office for E. Diabetic retinopathy screening: a short guide: increase effectiveness, maximize benefits and minimize harm. Copenhagen: World Health Organization. Regional Office for Europe; 2020.
    1. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316(22):2402–10. - PubMed

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