Journal of endodontics · 2026
Non-destructive Prediction of Remaining Useful Life for Nickel-Titanium Rotary Files Using Electrochemical Impedance Spectroscopy and Artificial Intelligence.
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Ma Yueling, Chen Kaige, Xu Yuhao, Yang Yichen, Wang Xiangzhu, He Xiaoyu
Аннотация
INTRODUCTION: Nickel-titanium (NiTi) rotary files are widely used in modern root canal therapy. Although instrument separation is relatively uncommon, sudden fatigue fracture may lead to serious clinical consequences. Current methods are limited to detecting macroscopic defects and cannot quantitatively assess early-stage microscopic fatigue damage. To address this issue, we combined electrochemical impedance spectroscopy (EIS) with artificial intelligence to develop a framework for the non-destructive prediction of the remaining useful life (RUL) of NiTi rotary files.
METHODS: We conducted EIS monitoring on 94 NiTi rotary files from their pristine state until fatigue fracture, and built a dataset containing 864 valid measurements. We designed a frequency point and sliding window joint optimization strategy to select key features and eliminate feature dimensionality redundancy. Using five-fold cross-validation, we evaluated 18 models: nine traditional machine-learning models, eight conventionally trained deep-learning architectures, and the pretrained tabular foundation model TabPFN; we established a three-level clinical risk-warning system.
RESULTS: Among the evaluated models, CatBoost, a gradient-boosting decision-tree algorithm, achieved the lowest cross-validated mean absolute error (MAE; 0.133 ± 0.015) and a mean three-level risk-classification accuracy of 71.1% ± 2.9%. Furthermore, the joint optimization strategy reduced feature dimensionality by 83.0% without significantly compromising performance. Best-performing traditional machine-learning models outperformed conventionally trained deep-learning architectures and TabPFN on this small tabular dataset.
CONCLUSIONS: This study confirms that an EIS- and AI-based non-destructive assessment method enables the quantitative evaluation of the fatigue state of NiTi rotary files prior to fracture, providing a clinically meaningful basis for personalized instrument replacement decisions.
METHODS: We conducted EIS monitoring on 94 NiTi rotary files from their pristine state until fatigue fracture, and built a dataset containing 864 valid measurements. We designed a frequency point and sliding window joint optimization strategy to select key features and eliminate feature dimensionality redundancy. Using five-fold cross-validation, we evaluated 18 models: nine traditional machine-learning models, eight conventionally trained deep-learning architectures, and the pretrained tabular foundation model TabPFN; we established a three-level clinical risk-warning system.
RESULTS: Among the evaluated models, CatBoost, a gradient-boosting decision-tree algorithm, achieved the lowest cross-validated mean absolute error (MAE; 0.133 ± 0.015) and a mean three-level risk-classification accuracy of 71.1% ± 2.9%. Furthermore, the joint optimization strategy reduced feature dimensionality by 83.0% without significantly compromising performance. Best-performing traditional machine-learning models outperformed conventionally trained deep-learning architectures and TabPFN on this small tabular dataset.
CONCLUSIONS: This study confirms that an EIS- and AI-based non-destructive assessment method enables the quantitative evaluation of the fatigue state of NiTi rotary files prior to fracture, providing a clinically meaningful basis for personalized instrument replacement decisions.