Related Experiment Video
Updated: Jun 20, 2026
Augmented Reality Navigation-Guided Core Decompression for Osteonecrosis of Femoral Head
Published on: April 12, 2022
Advanced systems for intraoperative cartilage evaluation and treatment demonstrate early feasibility and a shift
Logan D Moews1, Samuel A Alfonsi1, Volker Musahl2
1Midwest Orthopaedics at Rush University Medical Center, Chicago, Illinois, USA.
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.
