Machine learning models for predicting tibial intramedullary nail length.

BACKGROUND

Tibial intramedullary nailing (IMN) represents a standard treatment for fractures of the tibial shaft. Nevertheless, accurately predicting the appropriate nail length prior to surgery remains a challenging endeavour. Conventional techniques frequently depend on data obtained intraoperatively, which may prolong surgical time and elevate radiation exposure. This study employs anthropometric measurements to evaluate and contrast the efficacy of machine learning (ML) models in predicting tibial IMN length.

METHODS

A retrospective analysis was conducted on 163 patients who had undergone tibial IMN. Anthropometric data were collected, including the subject's height, shoe size, olecranon-to-5th metacarpal distance (OM), and tibial tuberosity-to-medial malleolus distance (TTMM). Four ML models, namely linear regression, random forest, decision tree, and XGBoost, were employed for the purpose of predicting tibial IMN length. The performance of the models was evaluated using the mean squared error (MSE) and the R-squared values.

RESULTS

The linear regression model demonstrated superior performance compared to the random forest, decision tree, and XGBoost models, with an R-squared value of 0.89, an MSE of 117.53, and a root mean squared error (RMSE) of 10.84 mm. The strongest correlation with IMN length was demonstrated by TTMM (r = 0.911), followed by height (r = 0.899) and OM (r = 0.811). Furthermore, TTMM provided the greatest contribution to prediction accuracy, thereby supporting its use as a reliable predictor in clinical settings. The correlation between shoe size and the dependent variable was weaker (r = 0.823), and the inclusion of shoe size in the model negatively impacted the prediction accuracy. Despite their ability to handle non-linear relationships, the random forest and XGBoost models yielded higher MSE values, indicating limited improvement over linear regression. These findings underscore the linear nature of the relationship between anthropometric variables and IMN length, with linear regression offering the most reliable predictions.

CONCLUSION

Combining anthropometric measurements with ML models, particularly linear regression, effectively predicts IMN length. This approach can streamline preoperative planning by reducing intraoperative measurements and minimizing surgery time and radiation exposure. Further validation with larger datasets is necessary to confirm these findings across diverse populations.