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In Journal of orthopaedic research : official publication of the Orthopaedic Research Society

Many studies have validated cartilage thickness as a biomarker for knee osteoarthritis (OA), however, few studies investigate beyond cross-sectional observations or comparisons across two timepoints. By characterizing the trajectory of cartilage thickness changes over 8 years in healthy individuals from the Osteoarthritis Initiative Dataset, this study discovers associations between the dynamics of cartilage changes and OA incidence. A fully automated cartilage segmentation and thickness measurement method was developed and validated against manual measurements: mean absolute error 0.11-0.14mm (n=4129 knees) and automatic reproducibility 0.04-0.07mm (n=316 knees). Mean thickness for the medial and lateral tibia (MT, LT), central weight-bearing medial and lateral femur (cMF, cLF), and patella (P) cartilage compartments were quantified for 1453 knees at 7 timepoints. Trajectory subgroups were defined per cartilage compartment as: stable, thinning to thickening, accelerated thickening, plateaued thickening, thickening to thinning, accelerated thinning, or plateaued thinning. For tibiofemoral compartments, the stable (22-36%) and plateaued thinning (22-37%) trajectories were the most common, with average initial velocity [μm/month], acceleration [μm/month2 ] for the plateaued thinning trajectories LT -2.66, 0.0326; MT -2.49, 0.0365; cMF -3.51, 0.0509; cLF -2.68, 0.041. In the patella compartment, the plateaued thinning (35%) and thickening to thinning (24%) trajectories were the most common, average initial velocity, acceleration for each -4.17, 0.0424; 1.95, -0.0835. Knees with non-stable trajectories had higher adjusted odds of OA incidence than stable trajectories: accelerated thickening, accelerated thinning, and plateaued thinning trajectories of the MT had adjusted OR of 18.9, 5.48, and 1.47 respectively; in the cMF, adjusted OR of 8.55, 10.1, and 2.61. This article is protected by copyright. All rights reserved.

Iriondo Claudia, Liu Felix, CalivĂ  Francesco, Kamat Sarthak, Majumdar Sharmila, Pedoia Valentina


cartilage thickness, deep learning, osteoarthritis, trajectory analysis