Towards streamlining orthopedic consultations: Machine learning classification of knee diagnosis groups via computer-assisted history taking

Comprehensive Summary

In this study, Oosterhoff et al. developed and validated a machine learning model (MLM) to predict a knee diagnosis group in patients prior to orthopedic consultation. 1172 patients were included in the study, and the dataset was split into training (n=938) and test (n=234) sets. 5 ML algorithms were tested: Random Forest (RF), Decision Tree (DT), Adaptive Boosting (AdaBoost), Support Vector Machine (SVM), and Penalized Logistic Regression (PLR). Using patient histories, the Random Forest model was able to identify that age, sports trauma, BMI, duration of knee complaints, and pain at rest were among important variables for the prediction of knee diagnosis category. In terms of algorithm performance, SVM scored the highest, with an accuracy score of 0.84, area under the curve (AUC) of 0.92, and precision of 0.85. Additionally, SVM accurately predicted 150 out of 153 knee osteoarthritis cases, giving it a sensitivity score of 0.98. Overall, the use of this predictive model shows potential for improving the efficiency in orthopedic practice, though broader validation is required to enhance and widen its clinical applicability.

Outcomes and Implications

Implementing a machine learning model to assist in predicting a knee diagnosis before orthopedic consultation has potential to improve both clinical efficiency and patient outcomes. If physicians are provided with a preliminary idea of diagnosis, they may be able to focus more on earlier treatment planning and patient care. This approach could reduce diagnostic burden, streamline clinical workflows, and lead to stronger outcomes for patients. While the MLM in this study demonstrated promising accuracy, further refinement and more testing will be needed to ensure that the model is suitable for widespread clinical use.