Automated Objective Scoring of Osteoarthritis Severity in Mouse Medial Tibial Cartilage Using Deep Learning

Abstract

Objective This study developed and optimized a deep learning model to automate OARSI-based histologic scoring of mouse medial tibial cartilage.Design Safranin-O-stained cartilage images were obtained from mice with OA induced by medial meniscus instability. A total of 2,788 images from 1,000 knees of 520 mice were included for model development and evaluation. Each data set was evaluated using deep learning models with multiple selection criteria for the reference standard. For preprocessing, horizontal cartilage alignment was learned using a VGG16-based regression model, and the tibial cartilage region was detected using YOLO-v7. These learned weights were applied to generate a rotation- and crop-adjusted cartilage data set, which was used to evaluate three CNNs.Results Initial classification of 544 mice cartilage images showed low accuracy, leading to an expansion of the data set to 2,788 images. An algorithm was applied to align the images horizontally and crop only the joint region, thereby reducing misclassification of noncartilaginous regions. This approach significantly improved the accuracy of cartilage degradation scoring. Among the deep learning models evaluated, VGG16 showed the best performance, achieving an MAE of 0.33. The model also recorded a precision of 0.680 (95% CI: 0.668-0.693), recall of 0.645 (95% CI: 0.627-0.664), F1-score of 0.653 (95% CI: 0.636-0.671), and accuracy of 0.648 (95% CI: 0.631-0.665).Conclusion In this study, the VGG16 model showed high concordance with expert assessments, suggesting the feasibility of automating OA grading from histological images in large-scale animal studies.