ArticleJournal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology2025
Comparative analysis of machine learning algorithms for predicting tibial intramedullary nail length from patient characteristics.
Article in Journal of orthopaedics and traumatology : official journal of the Italian Society of Orthopaedics and Traumatology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveThis study aimed to evaluate the performance of five machine learning algorithms in predicting tibial intramedullary nail length using patient demographic data (gender, height, age, and weight), with the goal of developing a clinically relevant and accurate predictive model.
methodsRetrospective data from 155 patients who underwent tibial intramedullary nailing at the Affiliated Jiangyin Hospital of Nantong University were analyzed. After data cleaning, outlier handling, and gender encoding, the dataset was divided into an 80% training set and 20% testing set. Models were trained and evaluated using root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R
resultsThe XGBoost model demonstrated superior clinical precision, achieving the lowest testing RMSE (9.15 mm) and MAE (7.56 mm), with an R
conclusionsMachine learning, particularly XGBoost, significantly improves preoperative prediction of tibial intramedullary nail length compared with traditional methods. LEVEL OF EVIDENCE IV:
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