{
  "abstract": "Background: Deep learning (DL) is increasingly applied to arthroscopic video for instrument recognition, anatomical segmentation, and intraoperative event detection. The performance, architectures, and real-time feasibility of these systems remain incompletely characterized. This systematic review aimed to evaluate the performance, architectures, and real-time feasibility of DL for classification, segmentation, and detection in arthroscopic video interpretation.\n\nMethods: Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2020, PubMed/ MEDLINE, Ovid, and the Cochrane Library were searched (February 2025), supplemented by artificial intelligence-assisted queries (Elicit, Undermind). Eligible studies applied DL to arthroscopy for classification, segmentation, or detection with quantitative performance and/or inference speed. Two reviewers independently screened, extracted, and assessed quality using Quality Assessment of Diagnostic Accuracy Studies-2; the protocol was registered in International Prospective Register of Systematic Reviews.\n\nResults: Six studies met inclusion criteria (knee = 2, shoulder = 2, wrist = 1, non-site-specific bleeding = 1). Classification achieved high accuracy for instrument recognition (up to 99.1%, sensitivity = 0.988) and bleeding- frame detection (95.8%, sensitivity = 97.3%). Segmentation was strongest for large bony structures (Dice 0.79-0.89) and weakest for deformable tissues (meniscus Dice = 0.48). U-Net variants (U-Net++, DeepLab v3+) predominated with frequent transfer learning. Two studies achieved real-time inference (15-60 frames per second). Risk of bias was high across all studies.\n\nConclusion: Based on 6 heterogeneous studies, DL appears technically feasible for arthroscopic image interpretation, particularly instrument classification and osseous segmentation, though findings should be interpreted as preliminary. No study evaluated impact on surgical decision-making or patient outcomes, and none was deployed prospectively in live surgery. Translation will require multicentre datasets, standardized reporting, and prospective evaluation against clinically meaningful endpoints.\n\nCite this article as: Lasslett C, Reddy R, Giritharan G, et al. Deep learning for real-time image interpretation in arthroscopy: a systematic review of classification, segmentation, and detection tasks. Eurasian J Med. 2026, 58(4), 1415, doi: 10.5152/ eurasianjmed.2026.261415.",
  "abstract_shown": true,
  "affiliations": [
    "Imperial College London Faculty of Medicine, London, UK",
    "Department of Radiology, Alexandria University Faculty of Medicine, Alexandria, Egypt",
    "Department of Radiology, Al-Azhar University Faculty of Medicine, Cairo Governorate, Egypt",
    "Ashford and St. Peter's Hospitals NHS Foundation Trust, Surrey, UK ; Smart Health Center, University of East London, London, UK"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 8,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Christopher Lasslett",
      "orcid": "0009-0003-9049-9801"
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    {
      "affs": [
        0
      ],
      "name": "Rohan Reddy",
      "orcid": "0009-0007-7906-5286"
    },
    {
      "affs": [
        0
      ],
      "name": "Gayathri Giritharan",
      "orcid": "0009-0008-2610-1666"
    },
    {
      "affs": [
        0
      ],
      "name": "Stylianos Tamasios",
      "orcid": "0009-0001-7050-8761"
    },
    {
      "affs": [
        0
      ],
      "name": "Ravit Dewan",
      "orcid": "0009-0000-0479-7393"
    },
    {
      "affs": [
        1
      ],
      "name": "Mohamed A. Khalafallah",
      "orcid": "0009-0000-1454-2589"
    },
    {
      "affs": [
        2
      ],
      "name": "Atef A. Hassan",
      "orcid": "0000-0003-3393-9105"
    },
    {
      "affs": [
        3
      ],
      "name": "Mohamed A. Imam",
      "orcid": "0000-0002-3646-809X"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/92hsa84538qke.bib",
  "cited_by": 0,
  "doi": "10.5152/eurasianjmed.2026.261415",
  "fulltext_url": "https://eajm.org/index.php/pub/article/view/3340",
  "id": "92hsa84538qke",
  "issns": [
    "1308-8742",
    "1308-8734"
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  "landing_url": "https://doi.org/10.5152/eurasianjmed.2026.261415",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "lcc",
      "R5-920"
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    [
      "openalex",
      "1707|Medical Image Segmentation Techniques"
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  "openalex": "W7225319063",
  "pdf_url": "https://www.eajm.org/index.php/pub/article/download/3340/3323",
  "pmcid": null,
  "pmid": null,
  "published": "2026-11-06",
  "publisher": "Atatürk University",
  "ris": "https://research.draconitic.com/paper/92hsa84538qke.ris",
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    {
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  "subjects": [
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      "field": "medicine",
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  "title": "Deep Learning for Real-Time Image Interpretation in Arthroscopy: A Systematic Review of Classification, Segmentation, and Detection Tasks",
  "title_text": "Deep Learning for Real-Time Image Interpretation in Arthroscopy: A Systematic Review of Classification, Segmentation, and Detection Tasks",
  "type": "peer-reviewed",
  "updated_at": "2026-11-08T14:20:17Z",
  "url": "https://research.draconitic.com/paper/92hsa84538qke",
  "venue": "Eurasian Journal of Medicine",
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}