{
  "abstract": "Aim\n\nThis study aimed to determine the effectiveness of deep learning models for detecting and predicting diabetes related kidney disease (DRKD) using two-dimensional retinal images.Methods\n\nA systematic review was conducted across eight databases from inception to July 14, 2026. Eligible cross-sectional and longitudinal studies assessed deep learning models for DRKD detection or prediction using retinal images alone, clinical data alone, or hybrid models. Data on study design, model development, validation, and performance metrics were extracted, with study quality appraised using QUADAS-2 and post hoc, PROBAST+AI. Results were synthesized narratively, with random effects meta analysis for hybrid models.Results\n\nOf 367 studies, six met inclusion criteria. Three cross-sectional studies of hybrid models contributed to the meta-analysis for DRKD detection, yielding pooled AUC 0.88, sensitivity 0.80, and specificity 0.81, though heterogeneity was high (I2 93.7%-99.8%). Three further studies were synthesized narratively rather than pooled: two cross-sectional studies used more severe or narrower CKD case definitions (AUC 0.86 and 0.96 respectively), and only one longitudinal study predicted the 5-year risk of DRKD (AUC 0.81, sensitivity 0.70, and specificity 0.77). External validation showed hybrid models consistently outperformed retinal-only (ΔAUC 0.03-0.11), but their advantage over clinical-only models was inconsistent.Conclusion\n\nDeep learning models may be useful for detecting and predicting DRKD from retinal images, with or without clinical data. However, the small number of heterogenous studies restricts generalizability. Future work should prioritize robust model development, and validation using large, diverse populations.",
  "abstract_shown": true,
  "affiliations": [
    "Centre for Intelligent Healthcare, Coventry University, Coventry, UK.",
    "Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Melbourne, Victoria, Australia.",
    "King's Ophthalmology Research Unit (KORU), Section of Ophthalmology, Faculty of Life Sciences and Medicine, King's College London, London, UK.",
    "School of Computing, Engineering & Digital Technologies, Teesside University, Middlesbrough, UK.",
    "Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.",
    "Diabetes Research Group, Medical School, Swansea University, Swansea, UK.",
    "Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Victoria, Australia."
  ],
  "article_type": "systematic review",
  "arxiv": null,
  "author_total": 9,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Samuel Ato Gyasi Otabir",
      "orcid": "0009-0005-4295-6443"
    },
    {
      "affs": [
        1
      ],
      "name": "Teketo Kassaw Tegegne",
      "orcid": "0000-0002-9137-3632"
    },
    {
      "affs": [
        2
      ],
      "name": "Paul Nderitu",
      "orcid": "0000-0002-8571-0751"
    },
    {
      "affs": [
        3
      ],
      "name": "Jiangtao Wang",
      "orcid": "0000-0002-8704-502X"
    },
    {
      "affs": [
        4
      ],
      "name": "Jonathan E Shaw",
      "orcid": "0000-0002-6187-2203"
    },
    {
      "affs": [
        5
      ],
      "name": "David Owens",
      "orcid": "0000-0003-1002-1238"
    },
    {
      "affs": [
        5
      ],
      "name": "Rebecca Thomas",
      "orcid": "0000-0002-2970-6352"
    },
    {
      "affs": [
        0,
        6
      ],
      "name": "Robyn Jennifer Tapp",
      "orcid": "0000-0002-8553-2684"
    },
    {
      "affs": [
        1
      ],
      "name": "Ralph Maddison",
      "orcid": "0000-0001-8564-5518"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/egk7j3pxjttvr.bib",
  "cited_by": 0,
  "doi": "10.1111/1753-0407.70271",
  "fulltext_url": "https://europepmc.org/articles/PMC13678574",
  "id": "egk7j3pxjttvr",
  "issns": [
    "1753-0393",
    "1753-0407"
  ],
  "landing_url": "https://europepmc.org/article/MED/42895056",
  "language": "eng",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2741|Retinal Imaging and Analysis"
    ]
  ],
  "openalex": "W7222983857",
  "pdf_url": "https://europepmc.org/articles/PMC13678574?pdf=render",
  "pmcid": "PMC13678574",
  "pmid": "42895056",
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/egk7j3pxjttvr.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:19:37Z",
      "native_id": "MED:42895056",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42895056"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Retinal Imaging and Analysis",
      "subfield": "imaging"
    }
  ],
  "title": "Detection and Risk Prediction of Diabetes Related Kidney Disease Using Retinal Images: A Systematic Review and Meta-Analysis.",
  "title_text": "Detection and Risk Prediction of Diabetes Related Kidney Disease Using Retinal Images: A Systematic Review and Meta-Analysis.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:19:37Z",
  "url": "https://research.draconitic.com/paper/egk7j3pxjttvr",
  "venue": "Journal of diabetes",
  "versions": []
}