Automated ACL Footprint Identification Using 3D Deep Learning
2026-08-18 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors studied how to accurately find the correct spot for the ACL ligament on the thigh bone using 3D MRI images. They created two AI models: one that looks at 3D bone shapes and another that looks directly at the MRI images. Both models worked well, but the one using MRI images was more accurate by about 0.7 mm. This approach could help surgeons place ACL grafts more precisely, which might reduce surgery failure and joint problems. Their work shows that advanced AI can assist with important steps in knee surgery planning.
Anterior Cruciate Ligament (ACL)Femoral TunnelACL Footprint3D Deep LearningMagnetic Resonance Imaging (MRI)Graph Convolutional Neural NetworkKnee Joint MechanicsACL ReconstructionGraft Failure PreventionOrthopedic Surgery
Authors
Ruida Cheng, Ali Uneri, Gabriel Gibson, Frances T. Sheehan, Barry Boden
Abstract
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.