# A Three-Stage Fusion Neural Network for Predicting the Risk of Root Fracture—A Pilot Study

## TL;DR

This pilot study introduces a three-stage fusion neural network to predict root fracture risk after dental treatment, achieving better accuracy than existing methods.

## Contribution

A novel three-stage fusion neural network is proposed to integrate categorical and numerical dental data for improved fracture risk prediction.

## Key findings

- The TSFNN achieved 82.1% accuracy in predicting root fractures.
- The model improved the F1-score by 19.1% compared to existing methods.
- The approach is effective even with limited clinical data.

## Abstract

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.