Abstract
Instrument separation in endodontic treatment constitutes a major problem that can hamper successful treatment and long-term survival. Usually, the risk process depends on a clinician's experience, X-ray evaluation, and his own judgment, which are very subjective and may be imprecise in predicting a possible instrument failure. Nowadays, artificial intelligence (AI) presents itself as a good solution, capitalizing on the capacity to analyze massive datasets, unearth hidden patterns, and generate predictive models with greater accuracy. Machine learning and deep learning methods may be able to synthesize parameters such as canal morphology, instrument design, operator variables, and clinical imaging in order to arrive at a prediction as to the chance of separation. Such AI-based decision tools can assist diagnostic precision, reducing errors, and allowing interventions leading to better clinical outcomes. This article reviews AI application in the prediction of risk factors for instrument separation, laying emphasis on the potential benefits, limitations, and consequences for the future of endodontic practice.
Keywords
Artificial Intelligence; Endodontics; Instrument Separation; Risk Prediction; Machine Learning; Deep Learning; Canal Morphology; Clinical Decision Support.
Citations
IRE Journals:
Elena Petrova , Markus Schneider
"Use of AI for Identifying Instrument Separation Risks" Iconic Research And Engineering Journals Volume 6 Issue 3 2022 Page 322-328
IEEE:
Elena Petrova , Markus Schneider
"Use of AI for Identifying Instrument Separation Risks" Iconic Research And Engineering Journals, 6(3)
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