Deep learning applications in post-operative ACL reconstruction imaging: a review of models, effectiveness, and clinical challenges

Anterior cruciate ligament reconstruction (ACLR) is widely used to restore knee stability. Accurate post-operative assessment—especially of tunnel morphology and surgical hardware placement is essential for evaluating outcomes. However, conventional image interpretation using MRI or CT is time-consuming and subject to variability. DL models like ResNet50-Unet, YOLOv8, ACLNet, and CNN-based classifiers demonstrated high accuracy in tunnel segmentation, tibial slope measurement, and tear detection. Multimodal approaches further improved diagnostic performance. However, limitations include small datasets, imaging variability, lack of external validation, and limited integration into clinical workflows. DL offers strong potential for enhancing post-ACLR imaging analysis through improved accuracy and automation. Real-world adoption depends on overcoming technical and clinical barriers, such as data standardization, interpretability, and validation in diverse populations.