A Deep Learning Automated Segmentation Algorithm Accurately Detects Differences in Longitudinal Cartilage Thickness Loss – Data from the FNIH Biomarkers Study of the Osteoarthritis Initiative

OBJECTIVE To study the longitudinal performance of fully automated cartilage segmentation in knees with radiographic osteoarthritis (ROA). We evaluate the sensitivity to change in progressor knees from the Foundation National Institutes of Health OA Biomarkers Consortium between the automated and previously reported manual expert segmentation, and whether differences in progression rates between predefined cohorts can be detected by the fully automated approach. METHODS The Osteoarthritis Initiative Biomarker Consortium was a nested case-control study. Progressor knees had both medial tibiofemoral radiographic joint space width loss (≥0.7 mm) and a persistent increase in WOMAC pain (≥9 on a 0-100 scale) after two years from baseline (n=194), whereas non-progressor knees did not have either of both (n=200). Deep learning automated algorithms trained on ROA or healthy reference (HRC) knees were used to automatically segment medial (MFTC) and lateral femorotibial cartilage on baseline and two-year follow-up MRIs. Findings were compared with previously published manual expert segmentation. RESULTS The MFTC cartilage loss in the progressor cohort was -181±245µm by manual (SRM=-0.74), -144±200µm by ROA-based model (SRM=-0.72), and -69±231µm by HRC-based model segmentation (SRM=-0.30). The Cohen's D for rates of progression between progressor vs. non-progressor cohort was -0.84 (p<0.001) for manual, -0.68 (p<0.001) for automated ROA-model, and -0.14 (p=0.18) for automated HRC-model segmentation. CONCLUSIONS A fully automated deep learning segmentation approach not only displayed similar sensitivity to change of longitudinal cartilage thickness loss in knee OA as manual expert segmentation, but also effectively differentiates longitudinal rates of cartilage thickness loss between cohorts with different progression profiles.