ArticleArchives of public health = Archives belges de sante publique2025
Assessing adoption of human and AI-enabled diabetic retinopathy screening in primary healthcare settings: findings from a pragmatic trial.
Article in Archives of public health = Archives belges de sante publique, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- A clinically interpretable machine learning model for early detection of diabetic retinopathy in multiple community health centers.Frontiers in endocrinology · 2026Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionDiabetic retinopathy (DR) screening with defined referral pathways is essential for early detection and effective management of DR. This study assessed the adoption of three DR screening (DRS) models in primary healthcare settings, focusing on referral adherence rates and stakeholders' perceptions of the interventions.
methodsA cross-sectional study was conducted in the Mohali district of Punjab, India, between February 2023 and January 2024. This pragmatic study compared three DRS arms (n = 200 each): I) facility-based screening at health and wellness centres (HWCs) by non-ophthalmologists, II) community-based AI DRS screening at home, and III) standard care involving counselling and referral to district hospitals (DHs). Participants with referable DR or ungradable images were advised for ophthalmology opinion, and their follow-up status and reasons for any non-compliance were assessed after one month. The adoption (acceptability and scalability) of the DRS was assessed via in-depth interviews with stakeholders involved in providing diabetes and DR care in public health settings.
resultsAmong the 600 participants screened, the average age was 58.22 years (SD ± 11.52). Most participants, 300 (59.57%), were aged 51-70 years, comprising 245 (40.77%) males and 355 (59.23%) females. The referral adherence rates were low, ranging from 13% to 17% across Arms I, II, and III. Barriers to follow-up included lack of awareness, financial limitations, health concerns, perceived good eye health, and transportation challenges. Qualitative findings reveal that DRS, implemented through HWCs and community-based models, is feasible and well-accepted by patients. Stakeholders largely supported the implementation of DRS within primary healthcare settings, though responses varied. Likewise, DRS through HWCs and community-based models is feasible and well-accepted among patients.
conclusionThe non-adherence to referrals among study participants is mainly attributable to economic constraints and knowledge gaps. Enhanced point-of-care counselling targeting groups at higher risk of non-adherence for follow-ups, along with a streamlined referral process, can improve the uptake of referral recommendations.
trial registrationClinical Trial Registry of India (CTRI): 2022/10/046283.
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