Subchondral bone alterations are emerging as considerable clinical problems associated with articular cartilage repair. Their analysis exposes a pattern of variable changes, including intra-lesional osteophytes, residual microfracture holes, peri-hole bone resorption, and subchondral bone cysts. A precise distinction between them is becoming increasingly important. Here, we present a tailored algorithm based on continuous data to analyse subchondral bone changes using micro-CT images, allowing for a clear definition of each entity. We evaluated this algorithm using data sets originating from two large animal models of osteochondral repair. Intra-lesional osteophytes were detected in 3 of 10 defects in the minipig and in 4 of 5 defects in the sheep model. Peri-hole bone resorption was found in 22 of 30 microfracture holes in the minipig and in 17 of 30 microfracture holes in the sheep model. Subchondral bone cysts appeared in 1 microfracture hole in the minipig and in 5 microfracture holes in the sheep model (n = 30 holes each). Calculation of inter-rater agreement (90% agreement) and Cohen’s kappa (kappa = 0.874) revealed that the novel algorithm is highly reliable, reproducible, and valid. Comparison analysis with the best existing semi-quantitative evaluation method was also performed, supporting the enhanced precision of this algorithm.
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