A hybrid approach to predicting and classifying dental impaction: integrating regularized regression and XG boost methods

A hybrid approach to predicting and classifying dental impaction: integrating regularized regression and XG boost methods

IntroductionDental impaction is a significant clinical challenge that requires advanced predictive modeling and healthcare analytics approaches. Impaction, a tooth alignment issue, is diagnosed using radiographic measurements like panoramic radiographs and CBCT. Artificial Intelligence (AI) is impro...

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Bibliographic Details
Main Authors: Asok Mathew, Pradeep K. Yadalam, Ahmed Radeideh, Shrouk Hady, Rona Swed, Reyyan Cheema, Majd Mousa AL-Mohammad, Mohammed Alsaegh, SR Shetty
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-04-01
Series:Frontiers in Oral Health
Subjects:
dental impaction
artificial intelligence
oral surgery
machine learning
ensemble learning
Online Access:https://www.frontiersin.org/articles/10.3389/froh.2025.1524206/full
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https://www.frontiersin.org/articles/10.3389/froh.2025.1524206/full

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