基于三维深度学习的前交叉韧带足迹自动识别
Automated ACL Footprint Identification Using 3D Deep Learning
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中文总结 AI 辅助
本研究开发两种三维深度学习模型，从三维MR图像识别ACL股骨足迹，基于图像的模型平均误差2.1mm优于基于网格的2.8mm，为ACL重建提供可行临床方法。
中文摘要 AI 辅助
前交叉韧带（ACL）重建失败的最常见原因之一是股骨隧道位置不当（ACL足迹中心及隧道方向），此类失败可能导致半月板病变和骨关节炎的发生。因此，准确识别ACL股骨足迹对于精准放置隧道、恢复膝关节原生力学、维持术后膝关节健康以及预防移植物失效至关重要。人工智能（AI）的最新进展为改进图像引导骨科手术带来了新机遇，但当前现有AI研究主要集中于基于术前和术后磁共振（MR）图像的ACL分割和破裂分类，利用深度学习方法识别ACL足迹中心的研究尚未得到充分探索。本研究旨在探索直接从三维MR图像识别ACL股骨足迹的三维深度学习模型，开发了两种综合三维深度学习架构：一种应用于三维股骨网格的基于三维图卷积神经网络的几何模型，另一种基于三维MR图像的三维地标增强识别模型。研究使用公开数据库中的4883组右膝和3087组左膝图像，其中80%用于模型构建，20%保留用于模型测试。两种模型均表现出色，但基于图像的方法优于基于网格的方法（平均误差2.1mm vs 2.8mm）。因此，三维深度学习为ACL足迹定位提供了可行的临床方法，有望提高ACL重建的足迹准确性。
英文摘要
One of the most common reasons for anterior cruciate ligament (ACL) reconstruction failure is femoral tunnel malpositioning (ACL footprint center and tunnel orientation). Such failures may lead to the development of meniscal pathology and osteoarthritis. Accurate ACL femoral footprint identification is therefore essential for precise tunnel placement, restoration of the native knee joint mechanics, post-surgical knee joint health and prevention of graft failure. Recent advances in artificial intelligence (AI) bring new opportunities to improve image-guided orthopedic surgery. However, at present, existing AI research focuses primarily on ACL segmentation and rupture classification based on pre- and post-operative magnetic resonance (MR) images. Identification of the ACL footprint center using deep learning methods has not been thoroughly researched. Thus, the purpose of this study is to explore 3D deep learning models for ACL femoral footprint identification directly from 3D MR images. Two comprehensive 3D deep learning architectures were developed: a 3D graph convolutional neural network-based geometric model applied to 3D femoral meshes; and a 3D landmark-enhanced identification model based on 3D MR images. A total of 4883 right and 3087 left knee image sets were used from a publicly available database. Eighty percent (80%) were applied to model generation, and twenty percent (20%) were preserved for model testing. Both models achieved excellent performance; however, the image-based method outperformed the model-based method (average error of 2.1mm vs 2.8 mm). Thus, 3D deep learning provides a feasible clinical approach for ACL footprint localization and has the potential to improve ACL reconstruction footprint accuracy.
