{
  "abstract": "Objectives To assess and compare the diagnostic accuracy of non-ophthalmologist-led diabetic retinopathy screening (DRS) at health and wellness centres (HWCs) and offline artificial intelligence (AI)-assisted community-based screening, using specialist grading as the reference standard in India.Design, settings and participants Pragmatic diagnostic accuracy study in primary healthcare settings. The settings included HWCs and community-based screening sites in rural Block Boothgarh, Mohali District, Punjab, India. A total of 600 people with diabetes aged ≥30 years were enrolled across three screening models: (1) non-ophthalmologist-led DRS at the HWC, (2) AI-assisted smartphone-based DRS in the community and (3) standard referral-based care. Retinal images were captured using non-mydriatic fundus cameras and independently graded by two masked human graders; a senior retina specialist resolved any disagreements. The AI was assessed for its ability to detect diabetic retinopathy (DR) and referable diabetic retinopathy (RDR). Diagnostic performance metrics were reported.Results The non-ophthalmologist-led model demonstrated 86.4% sensitivity (95% CI 65.1% to 97.1%) and 94.3% specificity (95% CI 88.5% to 97.7%) for DR detection, with an ungradability rate of 8%. For RDR, sensitivity reached 95.8% (95% CI 78.9% to 99.9%) and specificity was 93.1% (95% CI 88.0% to 96.5%). The offline AI-assisted model achieved 93.3% sensitivity (95% CI 68.1% to 99.8%) and 85.1% specificity (95% CI 76.9% to 91.2%) for RDR, but with a higher ungradability rate (38%), mainly due to cataracts and poor image quality. Both approaches effectively identified referable cases; however, the non-ophthalmologist-led model demonstrated greater accuracy and operational feasibility.Conclusions This study demonstrates that non-ophthalmologist-led DRS at HWCs can enhance access to primary care. Offline AI-enabled screening demonstrates potential for community use but is currently limited by image quality and binary classification outputs. Integrating both approaches may strengthen DRS coverage in resource-limited settings.Clinical trials registry of India CTRI/2022/10/046283.",
  "authors": [
    {
      "affiliations": [
        "Advanced Eye Centre, Post Graduate Institute of Medical Education and Research, Chandigarh, India"
      ],
      "name": "Anshul Chauhan"
    },
    {
      "affiliations": [
        "Global Health Economics Centre, London School of Hygiene & Tropical Medicine, London, UK"
      ],
      "name": "Luke Vale"
    },
    {
      "affiliations": [
        "Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India"
      ],
      "name": "Ankita Kankaria"
    },
    {
      "affiliations": [
        "Advanced Eye Centre, Post Graduate Institute of Medical Education and Research, Chandigarh, India"
      ],
      "name": "Basavaraj Tigari"
    },
    {
      "affiliations": [
        "Advanced Eye Centre, Post Graduate Institute of Medical Education and Research, Chandigarh, India"
      ],
      "name": "Sonam Kumar"
    },
    {
      "affiliations": [
        "Advanced Eye Centre, Post Graduate Institute of Medical Education and Research, Chandigarh, India"
      ],
      "name": "Mitesh Yadav"
    },
    {
      "affiliations": [
        "Advanced Eye Centre, Post Graduate Institute of Medical Education and Research, Chandigarh, India"
      ],
      "name": "Lakshay Kumar"
    },
    {
      "affiliations": [
        "Advanced Eye Centre, Post Graduate Institute of Medical Education and Research, Chandigarh, India"
      ],
      "name": "Gagandeep Kaur"
    },
    {
      "affiliations": [
        "Department of Endocrinology, Post Graduate Institute of Medical Education and Research, Chandigarh, India"
      ],
      "name": "Sanjay Kumar Bhadada"
    },
    {
      "affiliations": [
        "Advanced Eye Centre, Post Graduate Institute of Medical Education and Research, Chandigarh, India"
      ],
      "name": "Vishali Gupta"
    },
    {
      "affiliations": [
        "National Institute for Research in Digital Health and Data Science, Indian Council of Medical Research, New Delhi, Delhi, India"
      ],
      "name": "Mona Duggal"
    }
  ],
  "title": "Evaluation of non-ophthalmologist-led and offline AI-assisted models for diabetic retinopathy screening in India: a pragmatic diagnostic accuracy study",
  "uid": "c614e06c-cde4-5869-8358-68d1874c074e"
}