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Knee Joint

Knee Joint

The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris group...

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Prediction of the Serial Alignment Change after Opening-Wedge High Tibial Osteotomy Based on Coronal Plane Alignment

Joon Hee Cho¹, Hee Seung Nam¹, Seong Yun Park¹

¹Department of Orthopedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Bundang-gu, Seongnam-si, Gyeonggi-do, South Korea.

The Journal of Knee Surgery

|January 27, 2025

Summary

Machine learning accurately predicts knee alignment phenotypes after opening-wedge high tibial osteotomy (OWHTO). Postoperative joint line obliquity is the key predictor for final alignment, improving surgical outcomes.

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Area of Science:

Orthopedic surgery
Biomechanical analysis
Machine learning in medicine

Background:

Accurate prediction of postoperative knee alignment is crucial for successful outcomes after opening-wedge high tibial osteotomy (OWHTO).
Categorizing alignment into phenotypes aids in analyzing and predicting these changes.

Purpose of the Study:

To develop a machine learning model for predicting Coronal Plane Alignment of the Knee (CPAK) phenotypes post-OWHTO.
To identify key predictive factors influencing final alignment phenotypes.

Main Methods:

Retrospective analysis of 163 knees undergoing OWHTO, with data assessed preoperatively, at 3 months, and final follow-up.
Development of machine learning models using radiologic parameters and CPAK phenotypes.
Utilized Area Under the Curve (AUC) for model evaluation and Shapley Additive Explanation (SHAP) for feature importance.

Main Results:

The Multilayer Perceptron (MLP) model demonstrated high predictive performance (AUC 0.867 for radiologic parameters, 0.783 for CPAK phenotypes).
Postoperative joint line obliquity (JLO) at 3 months was the most significant radiologic predictor.
Constitutional and preoperative alignments also contributed, but 3-month postoperative features were strongest predictors.

Conclusions:

MLP models effectively predict final CPAK phenotypes after OWHTO.
Postoperative JLO is a critical radiologic parameter for predicting final knee alignment.
Combining constitutional, preoperative, and postoperative data enhances prediction accuracy for final alignment.