Comprehensive Summary
This study presents a machine learning model designed to assist in determining the appropriate surgical procedure for patients with end-stage knee osteoarthritis (KOA), specifically whether they should undergo total knee arthroplasty (TKA) or unicompartmental knee arthroplasty (UKA). The researchers utilized a dataset of 570 X-ray images, including anterior-posterior, lateral, and skyline views, to train three separate models based on the EfficientNet architecture. Each model was trained to output a probability for TKA or UKA, and these probabilities were averaged using a voting mechanism to generate a surgical recommendation. The model achieved an overall accuracy of 94.2% and an area under the curve (AUC) of 0.98. Individual models showed varying accuracies, with the anterior-posterior model achieving 90.4% accuracy, the lateral model 89.5%, and the skyline model 86.3%. Grad-CAM heatmaps indicated that the models focused on the knee joint area, suggesting that relevant anatomical features were used for decision-making. The study highlights the potential of integrating information from multiple X-ray views to improve surgical decision-making.
Outcomes and Implications
The introduction of this machine learning model has significant clinical implications for the management of knee osteoarthritis. By improving the accuracy of surgical procedure selection, the model can potentially reduce the risk of inappropriate surgeries, thereby decreasing revision rates and postoperative complications. This can lead to reduced healthcare costs and improved patient outcomes. The reliance on standard X-ray imaging makes the model accessible and implementable early in the patient evaluation process, facilitating more accurate referrals and streamlined preoperative planning. Additionally, the model can serve as a valuable resource for primary care providers and support physicians in low-resource settings, enhancing decision-making capabilities and patient care.