Effectiveness of Machine Learning in Predicting Orthodontic Tooth Extractions: A Multi-Institutional Study

Abstract

The study aimed to evaluate the effectiveness of machine learning in predicting whether orthodontic patients would require extraction or non-extraction treatment using data from two university datasets. A total of 1135 patients, with 297 from University 1 and 838 from University 2, were included during consecutive enrollment periods. The study identified 20 inputs including 9 clinical features and 11 cephalometric measurements based on previous research. Random forest (RF) models were used to make predictions for both institutions. The performance of each model was assessed using sensitivity (SEN), specificity (SPE), accuracy (ACC), and feature ranking. The model trained on the combined data from two universities demonstrated the highest performance, achieving 50% sensitivity, 97% specificity, and 85% accuracy. When cross-predicting, where the University 1 (U1) model was applied to the University 2 (U2) data and vice versa, there was a slight decrease in performance metrics (ranging from 0% to 20%). Maxillary and mandibular crowding were identified as the most significant features influencing extraction decisions in both institutions. This study is among the first to utilize datasets from two United States institutions, marking progress toward developing an artificial intelligence model to support orthodontists in clinical practice.