A Three-Stage Fusion Neural Network for Predicting the Risk of Root Fracture—A Pilot Study
2025
Yung-Ming Kuo | Liang-Yin Kuo | Hsun-Yu Huang | Tsen-Yu Sung | Chun-Hung Yang | Wan-Ting Chang | Chien-Shun Lo
Predicting the risk of root fractures following root canal therapy requires diagnosis of the dental history and status of patients. However, dental history is a kind of categorical data type that is not easy to combine with numerical data to obtain good performance in deep learning. The accuracy of support vector machine (SVM) and artificial neural networks (ANNs) is 71.7% and 73.1%, respectively. In this study, a three-stage fusion neural network (TSFNN) is proposed to improve the multiple types of clinical data in the dental field based on ANNs. Clinical data were obtained from 145 teeth, comprising 97 fractured teeth and 48 nonfractured teeth. Each dataset contained 17 items, which were divided into 10 categorical items and 7 numerical items. TSFNN combines numerical and categorical NN with batch normalization and embedding layer techniques and can produce the accuracy of 82.1% and a 19.1% improvement in F1-score. It shows impressive performance in predicting the risk of root fracture. Furthermore, due to the limited amount of clinical data, it is believed that such a pilot study can effectively improve the results when the amount of clinical data is insufficient.
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