Advanced systems for intraoperative cartilage evaluation and treatment demonstrate early feasibility and a shift towards integrating artificial intelligence: A scoping review
Logan D. Moews, Samuel A. Alfonsi, Volker Musahl, Michael T. Hirschmann, Brian J. Cole, Jorge Chahla, Kyle N. KunzeAbstract
Purpose
To review the current literature evaluating AI and advanced technologies for intraoperative cartilage management.
Methods
A 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.
Results
Seven 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.
Conclusion
Early 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.