in vitro assessment of AI-driven prediction for cyclic fatigue failure in NiTi rotary files

TL;DR

This paper evaluates an AI model's ability to predict when NiTi dental tools might break during use, aiming to improve safety in dental procedures.

Contribution

The study introduces a CNN-LSTM AI model that outperforms traditional methods in predicting NiTi rotary file fatigue failure.

Findings

The AI model achieved 94.2% accuracy in predicting cyclic fatigue failure in NiTi rotary files.

The model outperformed traditional prediction methods in sensitivity and specificity.

Real-time monitoring using AI could prevent instrument fractures during endodontic procedures.

Abstract

Nickel-titanium (NiTi) rotary files are also a significant clinical problem, which results in the separation of instruments during
endodontic work due to cyclic fatigue failure. Therefore, it is of interest validate an AI predictive model by utilizing real-time
operational parameters of five NiTi file systems. A CNN-LSTM was used to predict future failure based on the data on torque, angular
velocity, vibration, and temperature. The model had a high level of accuracy at 94.2 and sensitivity and specificity, which was better
than the methods of traditional prediction. Monitoring using AI is a promising solution to prevent NiTi files fracture in real-time to
make the endodontic process safer.