Osteoarthritis is a degenerative condition affecting bones and cartilage, often leading to osteophyte formation, bone density loss, and joint space narrowing. Treatment options to restore normal joint function vary depending on the severity of the condition. This work introduces an innovative deep-learning framework processing shoulder CT scans. It features the semantic segmentation of the proximal humerus and scapula, the 3D reconstruction of bone surfaces, the identification of the glenohumeral (GH) joint region, and the staging of three common osteoarthritic-related pathologies: osteophyte formation (OS), GH space reduction (JS), and humeroscapular alignment (HSA). The pipeline comprises two cascaded CNN architectures: 3D CEL-UNet for segmentation and 3D Arthro-Net for threefold classification. A retrospective dataset of 571 CT scans featuring patients with various degrees of GH osteoarthritic-related pathologies was used to train, validate, and test the pipeline. Root mean squared error and Hausdorff distance median values for 3D reconstruction were 0.22mm and 1.48mm for the humerus and 0.24mm and 1.48mm for the scapula, outperforming state-of-the-art architectures and making it potentially suitable for a PSI-based shoulder arthroplasty preoperative plan context. The classification accuracy for OS, JS, and HSA consistently reached around 90% across all three categories. The computational time for the inference pipeline was less than 15s, showcasing the framework's efficiency and compatibility with orthopedic radiology practice. The outcomes represent a promising advancement toward the medical translation of artificial intelligence tools. This progress aims to streamline the preoperative planning pipeline delivering high-quality bone surfaces and supporting surgeons in selecting the most suitable surgical approach according to the unique patient joint conditions.

翻译：骨关节炎是一种影响骨骼与软骨的退行性疾病，常导致骨赘形成、骨密度降低及关节间隙变窄。恢复正常关节功能的治疗方案因病情的严重程度而异。本研究提出了一种创新的深度学习框架，用于处理肩部CT扫描。该框架具备以下功能：近端肱骨与肩胛骨的语义分割、骨表面的三维重建、盂肱关节区域的识别，以及对三种常见骨关节炎相关病理的分期评估：骨赘形成、盂肱间隙减小和肱骨-肩胛骨对位关系。该流程包含两个级联的CNN架构：用于分割的3D CEL-UNet和用于三重分类的3D Arthro-Net。研究使用了一个包含571例具有不同程度盂肱关节骨关节炎相关病理特征的CT扫描回顾性数据集，对流程进行了训练、验证与测试。三维重建的均方根误差与豪斯多夫距离中位数在肱骨分别为0.22mm与1.48mm，在肩胛骨分别为0.24mm与1.48mm，其性能优于现有先进架构，使其可能适用于基于患者特异性植入物的肩关节置换术前规划场景。对于骨赘形成、关节间隙减小和对位关系三个类别的分类准确率均稳定达到约90%。推理流程的计算时间少于15秒，体现了该框架在骨科放射学实践中的高效性与兼容性。这些成果标志着人工智能工具向医学应用转化迈出了重要一步。该进展旨在优化术前规划流程，提供高质量的骨表面模型，并协助外科医生根据患者独特的关节状况选择最合适的手术方案。