Deep Learning for Real-Time Image Interpretation in Arthroscopy: A Systematic Review of Classification, Segmentation, and Detection Tasks
4 affiliations
- 1Imperial College London Faculty of Medicine, London, UK
- 2Department of Radiology, Alexandria University Faculty of Medicine, Alexandria, Egypt
- 3Department of Radiology, Al-Azhar University Faculty of Medicine, Cairo Governorate, Egypt
- 4Ashford and St. Peter's Hospitals NHS Foundation Trust, Surrey, UK ; Smart Health Center, University of East London, London, UK
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.
Methods: 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.
Results: 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.
Conclusion: 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.
Cite 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.
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BibTeX
@article{lasslett2026deep,
title = {{Deep Learning for Real-Time Image Interpretation in Arthroscopy: A Systematic Review of Classification, Segmentation, and Detection Tasks}},
author = {Christopher Lasslett and Rohan Reddy and Gayathri Giritharan and Stylianos Tamasios and Ravit Dewan and Mohamed A. Khalafallah and Atef A. Hassan and Mohamed A. Imam},
journal = {Eurasian Journal of Medicine},
year = {2026},
month = nov,
doi = {10.5152/eurasianjmed.2026.261415},
url = {https://doi.org/10.5152/eurasianjmed.2026.261415},
note = {CC BY},
}
RIS
TY - JOUR
TI - Deep Learning for Real-Time Image Interpretation in Arthroscopy: A Systematic Review of Classification, Segmentation, and Detection Tasks
AU - Christopher Lasslett
AU - Rohan Reddy
AU - Gayathri Giritharan
AU - Stylianos Tamasios
AU - Ravit Dewan
AU - Mohamed A. Khalafallah
AU - Atef A. Hassan
AU - Mohamed A. Imam
PY - 2026
DA - 2026/11/06
JO - Eurasian Journal of Medicine
DO - 10.5152/eurasianjmed.2026.261415
UR - https://doi.org/10.5152/eurasianjmed.2026.261415
AB - 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. Methods: 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. Results: 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. Conclusion: 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. Cite 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.
SN - 1308-8742
SN - 1308-8734
ER -