Prognosis of Anterior Cruciate Ligament (ACL) Reconstruction: A Data Driven Approach
Abhijit Chandra, Oliva Kar, Kuan-Chuen Wu, Michelle Hall, Jason, Gillette

TL;DR
This paper introduces a data-driven prognosis algorithm for ACL reconstruction patients that predicts knee instability and assesses recovery using in situ measurements during stair exercises, eliminating the need for controlled testing.
Contribution
It develops a multiscale, in situ measurement-based prognosis method for post-ACLR knee stability, enabling real-time assessment without offline training.
Findings
Predicts onset of knee instabilities using in situ data.
Identifies inefficient knee motions like varus and rotation.
Provides measures for recovery progress based on energy dissipation.
Abstract
Individuals who suffer anterior cruciate ligament (ACL) injury are at higher risk of developing knee osteoarthritis (OA) and almost 50% display symptoms 10 to 20 years post injury. Anterior cruciate ligament reconstruction (ACLR) often does not protect against knee OA development. Accordingly, a multiscale formulation for Data Driven Prognosis (DDP) of post ACLR is developed. Unlike traditional predictive strategies that require controlled off-line measurements or training for determination of constitutive parameters to derive the transitional statistics, the proposed DDP algorithm relies solely on in situ measurements. The proposed DDP scheme is capable of predicting onset of instabilities. Since the need for off line testing (or training) is obviated, it can be easily implemented for ACLR, where such controlled a priori testing is almost impossible to conduct. The DDP algorithm…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
