Automating Three-Dimensional Osteoarthritis Histopathological Grading of Human Osteochondral Tissue Using Machine Learning on Contrast-Enhanced Micro-Computed Tomography.

Objective To develop and validate a machine learning (ML) approach for automatic three-dimensional (3D) histopathological grading of osteochondral samples imaged with contrast-enhanced micro-computed tomography (CEμCT). Design Osteochondral cores from 24 total knee arthroplasty patients and 2 asymptomatic cadavers (n = 34, Ø = 2 mm; n = 45, Ø = 4 mm) were imaged using CEμCT with phosphotungstic acid-staining. Volumes-of-interest (VOI) in surface (SZ), deep (DZ) and calcified (CZ) zones were extracted depthwise and subjected to dimensionally reduced Local Binary Pattern-textural feature analysis. Regularized Ridge and Logistic regression (LR) models were trained zone-wise against the manually assessed semi-quantitative histopathological CEμCT grades (Ø = 2 mm samples). Models were validated using nested leave-one-out cross-validation and an independent test set (Ø = 4 mm samples). The performance was assessed using Spearman’s correlation, Average Precision (AP) and Area under the Receiver Operating Characteristic Curve (AUC). Results Highest performance on cross-validation was observed for SZ, both on Ridge regression (ρ = 0.68, p < 0.0001) and LR (AP = 0.89, AUC = 0.92). The test set evaluations yielded decreased Spearman’s correlations on all zones. For LR, performance was almost similar in SZ (AP = 0.89, AUC = 0.86), decreased in CZ (AP = 0.71→0.62, AUC = 0.77→0.63) and increased in DZ (AP = 0.50→0.83, AUC = 0.72→0.72). Conclusion We showed that the ML-based automatic 3D histopathological grading of osteochondral samples is feasible from CEμCT. The developed method can be directly applied by OA researchers since the grading software and all source codes are publicly available.