Abstract：
The bicipital groove is an important anatomical feature of the proximal humerus that needs to be identified during surgical planning for procedures such as shoulder arthroplasty and proximal humeral fracture reconstruction. Current algorithms for automatic identification prove ineffective in arthritic humeri due to the presence of osteophytes, reducing their usefulness for total shoulder arthroplasty. Our methodology involves the use of a Random Forest Classifier (RFC) to automatically detect the bicipital groove on segmented computed tomography scans of humeri. We evaluated our model on two distinct test datasets: one comprising non-arthritic humeri and another with arthritic humeri characterized by significant osteophytes. Our model detected the bicipital groove with a mean absolute error of less than 1mm on arthritic humeri, demonstrating a significant improvement over the previous gold standard approach. Successful identification of the bicipital groove with a high degree of accuracy even in arthritic humeri was accomplished. This model is open source and included in the python package shoulder.
摘要：
肱骨沟是肱骨近端的重要解剖特征，在肩关节成形术和肱骨近端骨折重建等手术的手术计划过程中需要识别。由于骨赘的存在，目前用于自动识别的算法在关节炎肱骨中无效，降低了它们对全肩关节置换术的有用性。我们的方法涉及使用随机森林分类器（RFC）在肱骨的分段计算机断层扫描扫描中自动检测二头肌沟。我们在两个不同的测试数据集上评估了我们的模型：一个包含非关节炎性肱骨，另一个包含以明显骨赘为特征的关节炎肱骨。我们的模型在关节炎肱骨上检测到二头肌沟，平均绝对误差小于1mm，证明比以前的金本位制方法有了显著的改进。即使在关节炎肱骨中，也可以高精度地成功识别二头肌沟。这个模型是开源的，包含在python包肩膀中。
