Safari Kosar, Rodriguez Vila Borja, Pierce David M
School of Mechanical, Aerospace, and Manufacturing Engineering, Storrs, Connecticut, USA.
Universidad Rey Juan Carlos, Medical Image Analysis and Biometry Laboratory, Madrid, Spain.
J Orthop Res. 2025 Jun;43(6):1101-1112. doi: 10.1002/jor.26071. Epub 2025 Mar 20.
Articular cartilage, essential for smooth joint movement, can sustain micrometer-scale microcracks in its collagen network from low-energy impacts previously considered non-injurious. These microcracks may propagate under cyclic loading, impairing cartilage function and potentially initiating osteoarthritis (OA). Detecting and analyzing microcracks is crucial for understanding early cartilage damage but traditionally relies on manual analyses of second harmonic generation (SHG) images, which are labor-intensive, limit scalability, and delay insights. To address these challenges, we established and validated a YOLOv8-based deep learning model to automate the detection, segmentation, and quantification of cartilage microcracks from SHG images. Data augmentation during training improved model robustness, while evaluation metrics, including precision, recall, and F1-score, confirmed high accuracy and reliability, achieving a true positive rate of 95%. Our model consistently outperformed human annotators, demonstrating superior accuracy, repeatability, all while reducing labor demands. Error analyses indicated precise predictions for microcrack length and width, with moderate variability in estimations of orientation. Our results demonstrate the transformative potential of deep learning in cartilage research, enabling large-scale studies, accelerating analyses, and providing insights into soft tissue damage and engineered material mechanics. Expanding our data set to include diverse anatomical regions and disease stages will further enhance performance and generalization of our YOLOv8-based model. By automating microcrack detection, this study advances understanding of microdamage in cartilage and potential mechanisms of progression of OA. Our publicly available model and data set empower researchers to develop personalized therapies and preventive strategies, ultimately advancing joint health and preserving quality of life.
关节软骨对关节的顺畅运动至关重要，它能承受胶原网络中因先前认为无损伤的低能量冲击而产生的微米级微裂纹。这些微裂纹可能在循环载荷下扩展，损害软骨功能并可能引发骨关节炎（OA）。检测和分析微裂纹对于理解早期软骨损伤至关重要，但传统上依赖于对二次谐波产生（SHG）图像的人工分析，这种分析劳动强度大，限制了可扩展性，并延迟了对问题的深入了解。为应对这些挑战，我们建立并验证了一个基于YOLOv8的深度学习模型，用于自动检测、分割和量化SHG图像中的软骨微裂纹。训练期间的数据增强提高了模型的鲁棒性，而包括精确率、召回率和F1分数在内的评估指标证实了该模型具有高准确性和可靠性，真阳性率达到95%。我们的模型始终优于人工标注者，展示出更高的准确性、可重复性，同时减少了人力需求。误差分析表明，该模型对微裂纹长度和宽度的预测精确，方向估计的变异性适中。我们的结果证明了深度学习在软骨研究中的变革潜力，能够开展大规模研究、加速分析，并为软组织损伤和工程材料力学提供深入见解。将我们的数据集扩展到包括不同的解剖区域和疾病阶段，将进一步提高基于YOLOv8的模型的性能和通用性。通过实现微裂纹检测自动化，本研究推进了对软骨微损伤及OA进展潜在机制的理解。我们公开可用的模型和数据集使研究人员能够开发个性化疗法和预防策略，最终促进关节健康并维护生活质量。
