ArticleKnee surgery, sports traumatology, arthroscopy : official journal of the ESSKA2026
Advanced systems for intraoperative cartilage evaluation and treatment demonstrate early feasibility and a shift towards integrating artificial intelligence: A scoping review.
Article in Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo review the current literature evaluating AI and advanced technologies for intraoperative cartilage management.
methodsA comprehensive search of PubMed, Embase, and Scopus was conducted in March 2026 according to PRISMA guidelines. Eligible studies included cadaveric, in-vivo, or clinical investigations using AI-based or computer navigation systems for real-time intraoperative diagnosis, mapping, or treatment of cartilage lesions. Studies limited to preoperative planning, static imaging segmentation, or non-surgical applications were excluded. Two reviewers screened studies, extracted data on design, population, technology type, and outcomes, and assessed risk of bias using CLAIM, QUADAS-2, or MINORS criteria. Findings were synthesised narratively.
resultsSeven studies met inclusion criteria. These included three studies evaluating AI-based cartilage mapping and segmentation systems, three assessing computer-assisted navigation systems, and one describing a hybrid system integrating mapping with navigation. AI-based segmentation and mapping systems demonstrated Dice coefficients of 0.68-0.90 and intersection-over-union scores up to 92%, with performance comparable to human reference masks but reduced accuracy in low-quality images. Navigation systems for osteochondral grafting reduced angular errors in graft harvest, coring, and placement from >12° freehand to <4° with navigation, and hybrid systems decreased plug orientation error from 15.4° to 6.5°. Stereo-endoscopic platforms achieved sub-millimetre 3D reconstruction but exceeded clinically acceptable orientation thresholds. Intraoperative 3D laser scanning achieved mean defect measurement error of 0.46 mm and reduced workflow times to <4 min compared with approximately 15 min conventionally.
conclusionEarly studies support the feasibility and accuracy of computer-assisted and navigation-based technologies, as well as AI-driven mapping, for real-time cartilage assessment and treatment. Further clinical evaluation is needed to establish safety and effectiveness in real-world surgical environments. LEVEL OF EVIDENCE: Level IV, scoping review.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.