To investigate the test–retest precision and to report the longitudinal change in cartilage thickness, the percentage of knees with progression and the predictive value of the machine-learning-estimated structural progression score (s-score) for cartilage thickness loss in the IMI-APPROACH cohort – an exploratory, 5-center, 2-year prospective follow-up cohort. Quantitative cartilage morphology at baseline and at least one follow-up visit was available for 270 of the 297 IMI-APPROACH participants (78% females, age: 66.4 ± 7.1 years, body mass index (BMI): 28.1 ± 5.3 kg/m2, 55% with radiographic knee osteoarthritis (OA)) from 1.5T or 3T MRI. Test–retest precision (root mean square coefficient of variation) was assessed from 34 participants. To define progressor knees, smallest detectable change (SDC) thresholds were computed from 11 participants with longitudinal test–retest scans. Binary logistic regression was used to evaluate the odds of progression in femorotibial cartilage thickness (threshold: −211 μm) for the quartile with the highest vs the quartile with the lowest s-scores. The test–retest precision was 69 μm for the entire femorotibial joint. Over 24 months, mean cartilage thickness loss in the entire femorotibial joint reached −174 μm (95% CI: −207, −141 μm, 32.7% with progression). The s-score was not associated with 24-month progression rates by MRI (OR: 1.30, 95% CI: 0.52, 3.28). IMI-APPROACH successfully enrolled participants with substantial cartilage thickness loss, although the machine-learning-estimated s-score was not observed to be predictive of cartilage thickness loss. IMI-APPROACH data will be used in subsequent analyses to evaluate the impact of clinical, imaging, biomechanical and biochemical biomarkers on cartilage thickness loss and to refine the machine-learning-based s-score. NCT03883568.