# Reach and implementation of human and AI-assisted diabetic retinopathy screening models in primary healthcare settings in India

## TL;DR

This study examines how well different diabetic retinopathy screening models work in rural India, finding that community-based AI-assisted screening is more effective than facility-based methods.

## Contribution

The study introduces a novel evaluation of DR screening models using the RE-AIM framework in a rural Indian setting.

## Key findings

- Community-based AI-assisted screening had lower refusal rates compared to facility-based screening.
- Barriers like mobility and perceived eye health affected participation, especially among older individuals.
- Technical adaptations improved implementation fidelity in resource-constrained settings.

## Abstract

Diabetic retinopathy (DR) is a leading cause of preventable vision loss. While DR screening is critical, evidence on the reach and implementation of different screening models in primary healthcare settings is limited. This study evaluated the reach and implementation of DRS models in northern India using the RE-AIM framework. A pragmatic three-arm observational study was conducted between February 2023 and January 2024 in Block Boothgarh, a rural block in District Mohali, Punjab, comprising 30 villages with an estimated 120,000 residents. Household line listing was performed to identify individuals aged 30 years or older with diabetes. Participants (n = 600) were equally allocated to three screening models: facility-based screening at Health and Wellness Centres (HWC) by non-ophthalmologists, community-based AI-assisted screening at home, and standard care. Reach and implementation were assessed through quantitative data, field observations, and qualitative interviews with healthcare providers. Refusal for screening was higher in facility-based screening (40%, 135/340) and lower in community-based screening (13%, 31/240). Older individuals were more likely to decline participation, with a mean age of 62.0 years for males and 60.3 years for females. Reported barriers included existing medical conditions, mobility limitations, perceived good eye health, travel distance, and transportation difficulties. Concerns regarding long-term medication adherence also reduced uptake. Technical issues, including power outages, hardware or software malfunctions, suboptimal image quality, and lack of cooperation, further declined implementation. Adaptations, including the use of backup power generators, on-site troubleshooting, and provision of transport support, mitigated these barriers and improved overall implementation fidelity. Assessing reach is essential for the success of public health interventions. Using the RE-AIM framework, this study identified key barriers and adaptive strategies in DRS, enhancing both reach and implementation within primary healthcare settings. These findings can inform the integration of DRS models into comparable resource-constrained contexts, thereby improving overall effectiveness.

Clinical Trial Registry of India (CTRI): 2022/10/046283.

The online version contains supplementary material available at 10.1038/s41598-025-25402-9.