Can machine learning predict the accuracy of preoperative planning for total hip arthroplasty, basing on patient-related factors? An explorative investigation on Supervised machine learning classification models.

Background

The success of Total Hip Arthroplasty (THA) is influenced by preoperative planning, with traditional 2D approaches displaying varied reliability as well. The present study investigates the use of Supervised Machine Learning (SML) models with patient-related features to improve accuracy.

Methods

Preoperative and perioperative data, as well as planning and final implant information, were obtained from 800 consecutive cementless primary THA, which was performed uniformly by a specialized surgical team. Six Supervised Machine Learning models were trained and validated using patient characteristics and implant data: Logistic Regression (LR), Linear Discriminant Analysis (LDA), K-Nearest Neighbors (KNN), Decision Tree (CART), Gaussian Naive Bayes (GN), and Support Vector Classifier (SVC). The models' ability to predict planning reliability and leg length disparity was evaluated.

Results

KNN performed better on the cup model (97.9 %), femur model (96.7 %), and femur size (99.2 %). SVM emerged as the model with the highest accuracy for cup size (60.4 %) and head size (62.1 %). CART had the best accuracy (99 %) when determining leg length discrepancy.

Conclusion

The study demonstrates the utility of Supervised Machine Learning models, specifically KNN, in predicting the accuracy of preoperative planning in THA. The accuracy of these models, which are driven by patient-related characteristics, provides useful information for optimizing patients' selection and improving surgical outcome.

Informations de copyright

© 2024 The Authors.

Déclaration de conflit d'intérêts

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.