{
  "abstract": "Introduction: Multiple investigations have been conducted to diagnose chronic kidney disease (CKD) from photographs of retina using deep learning models, with a wide range of diagnostic performance, which necessitates the importance of a comprehensive review. Therefore, in this meta-analysis, we estimated the detection performance of deep learning models for the diagnosis of CKD using retinal photographs.\n\nMethods: MEDLINE, Scopus, and Web of Science were searched until 25 December 2025. Pooled estimation of heterogeneity, diagnostic parameters, and meta-regressions was performed using MetaDisc and STATA Software packages.\n\nResults: The pooled sensitivity, specificity, and diagnostic odds ratio were 0.81 (95% CI, 0.80–0.82), 0.64 (95% CI, 0.64–0.65), and 9.15 (95% CI, 5.72–14.62), respectively. The pooled estimates of the positive and negative likelihood ratios were 2.18 (95% CI, 1.66–2.87) and 0.28 (95% CI, 0.20–0.39), respectively. The area under the summary receiver operating characteristic curve showed a discriminative performance of 0.77.\n\nConclusion: Deep learning based on retinal images had moderate diagnostic performance for detection of CKD. Relatively high sensitivity and modest specificity suggest that deep learning is more effective for ruling out CKD than for confirming the diagnosis that deep learning is more effective for ruling out CKD than for confirming the diagnosis. Deep learning-based retinal analysis may therefore serve as a supportive, noninvasive screening or triage tool but should not replace established diagnostic methods for CKD.",
  "abstract_shown": true,
  "affiliations": [
    "Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran",
    "ERIS Research Institute, Tehran, Iran",
    "Warwick Medical School, University of Warwick, Coventry, UK",
    "School of Medicine, Yasuj University of Medical Sciences, Yasuj, Iran",
    "Blood Transfusion Research Center, High Institute for Research and Education in Transfusion Medicine, Tehran, Iran",
    "Capital Medical University, Beijing, China",
    "Department of Anesthesiology, School of Medicine, Hamadan University of Medical Sciences, Hamadan, Iran",
    "Department of Biomedical Engineering, College of Engineering, University of Illinois at Chicago, Chicago, IL, USA"
  ],
  "article_type": "review article",
  "arxiv": null,
  "author_total": 15,
  "authors": [
    {
      "affs": [
        0,
        1
      ],
      "name": "Mehrdad Farrokhi"
    },
    {
      "affs": [
        1
      ],
      "name": "Behnam Hoorshad"
    },
    {
      "affs": [
        2
      ],
      "name": "Reza Fahimi"
    },
    {
      "affs": [
        1
      ],
      "name": "Atousa Moghadam Fard"
    },
    {
      "affs": [
        1
      ],
      "name": "Behnaz Dalvandi"
    },
    {
      "affs": [
        1
      ],
      "name": "Reza Dalvandi"
    },
    {
      "affs": [
        1
      ],
      "name": "Mahshid Esmaielzade Rostami"
    },
    {
      "affs": [
        3
      ],
      "name": "Reza Judi Chelan"
    },
    {
      "affs": [
        1
      ],
      "name": "Oveis Ahmadzadeh"
    },
    {
      "affs": [
        4
      ],
      "name": "Sareh Salarinejad"
    },
    {
      "affs": [
        5
      ],
      "name": "Ramtin Shemshadigolafzani"
    },
    {
      "affs": [
        1
      ],
      "name": "Morteza Alipour"
    },
    {
      "affs": [
        1
      ],
      "name": "Parham Rahmani"
    },
    {
      "affs": [
        6
      ],
      "name": "Marjan Lashgari"
    },
    {
      "affs": [
        7
      ],
      "name": "Mahya Asadalizadeh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/eehbcpdhvwqrh.bib",
  "cited_by": 0,
  "doi": "10.1159/ore/aedag003",
  "fulltext_url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13710867/",
  "id": "eehbcpdhvwqrh",
  "issns": [
    "0030-3747",
    "1423-0259"
  ],
  "landing_url": "https://doi.org/10.1159/ore/aedag003",
  "language": "en",
  "licence": "CC-BY-NC-4.0",
  "licence_label": "CC BY-NC 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "heading",
      "Retina"
    ],
    [
      "openalex",
      "2741|Retinal Imaging and Analysis"
    ]
  ],
  "openalex": "W7204442788",
  "pdf_url": null,
  "pmcid": "PMC13710867",
  "pmid": "42647424",
  "published": "2026-08-26",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/eehbcpdhvwqrh.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T12:21:44Z",
      "native_id": "PMC13710867",
      "source": "pmc",
      "url": "https://doi.org/10.1159/ore/aedag003"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Retinal Imaging and Analysis",
      "subfield": "imaging"
    }
  ],
  "title": "Diagnostic Performance of Deep Learning Using Retinal Images for Detection of Chronic Kidney Disease: A Systematic Review and Meta-Analysis",
  "title_text": "Diagnostic Performance of Deep Learning Using Retinal Images for Detection of Chronic Kidney Disease: A Systematic Review and Meta-Analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T12:21:44Z",
  "url": "https://research.draconitic.com/paper/eehbcpdhvwqrh",
  "venue": "Ophthalmic Research",
  "versions": []
}