{
  "abstract": "Objective\n\nTo assess the diagnostic accuracy of machine learning (ML) and deep learning (DL) models for endometriosis.Methods\n\nA systematic review and meta-analysis were conducted in accordance with PRISMA guidelines (PROSPERO: CRD420251231469). Databases were searched through November 24, 2025. Twenty-two eligible studies were included. Risk of bias was assessed using QUADAS-2. Pooled analysis was conducted in Stata 18.0.Results\n\nPooled sensitivity was 0.90 (95% CI 0.86-0.93), specificity 0.75 (95% CI 0.64-0.84), and SROC-AUC 0.92 (95% CI 0.89-0.94). A positive prediction increased disease probability from 10% to 29%, whereas a negative prediction reduced it to 1%. Multicenter design and DL were associated with lower sensitivity. Results were robust.Conclusions\n\nML/DL models demonstrate high diagnostic performance and are promising for non-invasive auxiliary diagnosis. Large multicenter prospective studies and multimodal models are needed to enable clinical translation.Systematic Review Registration: https://www.crd.york.ac.uk/PROSPERO/view/CRD420251231469, identifier CRD420251231469.",
  "abstract_shown": true,
  "affiliations": [
    "Chengdu University of Traditional Chinese Medicine , ,",
    "Department of Gynecology, Hospital of Chengdu University of Traditional Chinese Medicine , ,"
  ],
  "article_type": "systematic review",
  "arxiv": null,
  "author_total": 4,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Jiayi Guo"
    },
    {
      "affs": [
        0
      ],
      "name": "Xue Liu"
    },
    {
      "affs": [
        0
      ],
      "name": "Huiying Zhang"
    },
    {
      "affs": [
        1
      ],
      "name": "Shaobin Wei"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/c06w7s6723hv2.bib",
  "cited_by": 0,
  "doi": "10.3389/fmed.2026.1907483",
  "fulltext_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13716802/?tool=EBI",
  "id": "c06w7s6723hv2",
  "issns": [
    "2296-858X"
  ],
  "landing_url": "https://europepmc.org/article/PMC/PMC13716802",
  "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": [
    [
      "heading",
      "Systematic Review"
    ],
    [
      "openalex",
      "2743|Endometriosis Research and Treatment"
    ]
  ],
  "openalex": "W7222292401",
  "pdf_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13716802/pdf/?tool=EBI",
  "pmcid": "PMC13716802",
  "pmid": "42959151",
  "published": "2026-10-15",
  "publisher": "Frontiers Media SA",
  "ris": "https://research.draconitic.com/paper/c06w7s6723hv2.ris",
  "sources": [
    {
      "first_seen": "2026-10-31T00:20:37Z",
      "native_id": "PMC:PMC13716802",
      "source": "europepmc",
      "url": "https://europepmc.org/article/PMC/PMC13716802"
    },
    {
      "first_seen": "2026-11-01T00:20:52Z",
      "native_id": "MED:42959151",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42959151"
    },
    {
      "first_seen": "2026-11-01T12:24:00Z",
      "native_id": "PMC13716802",
      "source": "pmc",
      "url": "https://doi.org/10.3389/fmed.2026.1907483"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2743|Endometriosis Research and Treatment",
      "subfield": "reproductive-child"
    }
  ],
  "title": "Machine learning and deep learning for diagnosis of endometriosis: systematic review and meta-analysis",
  "title_text": "Machine learning and deep learning for diagnosis of endometriosis: systematic review and meta-analysis",
  "type": "peer-reviewed",
  "updated_at": "2026-11-02T12:21:14Z",
  "url": "https://research.draconitic.com/paper/c06w7s6723hv2",
  "venue": "Frontiers in medicine",
  "versions": []
}