Distribution of interscan measurement error in AI-based 3D MRI analysis of knee cartilage thickness in osteoarthritis

Hisako Katano, Eiji Sasaki, Kanto Nagai, Naofumi Hashiguchi, Haruka Kaneko, Yasuyuki Ishibashi, Ryosuke Kuroda, Nobuo Adachi, Muneaki Ishijima, Makoto Tomita, Jun Masumoto and Ichiro Sekiya

PLOS ONE, 2025, vol. 20, issue 8, 1-12

Abstract: Purpose: A novel AI-based 3D analysis system was developed to automatically extract bone and cartilage from MRI data and provide average cartilage thickness. This study aimed to analyze the interscan measurement error of knee cartilage thickness in osteoarthritis patients. Methods: Fifty knee osteoarthritis patients underwent two scans using MRI systems from five different vendors. Each model included five Kellgren-Lawrence grade (KL) 1–2 and five KL3–4 patients. Cartilage thickness was automatically quantified for seven regions, and interscan measurement error was analyzed. Results: In the KL1–2 group, measurements with errors ≤0.05 mm, ≤ 0.10 mm, and ≤0.20 mm were 42%, 75%, and 97%, respectively. For the KL3–4 group, these proportions were 31%, 59%, and 90%. The entire cohort (KL1–4) showed errors ≤0.05 mm, ≤ 0.10 mm, and ≤0.20 mm in 37%, 67%, and 93% of measurements. Differences between KL1–2 and KL3–4 groups were significant for all thresholds. Conclusion: Overall, 93% of interscan measurement errors were within 0.20 mm when using fully automatic MRI 3D analysis software to assess knee cartilage thickness in osteoarthritis patients. This study provides valuable insights into the reliability of automated cartilage thickness measurements across different disease severities and MRI systems.

Date: 2025
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DOI: 10.1371/journal.pone.0329610

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