Enhancing Endodontic Precision: A Novel AI-Powered Hybrid Ensemble Approach for Refining Treatment Strategies
Keywords:Endodontic treatments, Root canal curvature, Artificial intelligence (AI), Hybrid ensemble classifier
Root canal curvature and calcification present challenges during root canal treatment, increasing the risk of procedural mishaps. These factors can jeopardize the management of intra-radicular infection leading to unfavourable treatment outcomes. The present research introduces an innovative approach that utilizes artificial intelligence (AI) to enhance endodontic treatments. The study focuses on the development of a hybrid ensemble classifier, which combines multiple classification algorithms. By harnessing the strengths of these algorithms, the hybrid ensemble classifier improves the accuracy and robustness of classifying various endodontic challenges. The research also incorporates image segmentation techniques to isolate specific regions of interest, including teeth and roots, for further analysis. The segmentation process involves contrast enhancement, adaptive thresholding, contour detection and root segmentation. Through experimentation, the proposed approach demonstrates notable improvements in precision, recall, F1-score, accuracy and overall performance, ultimately refining endodontic treatments. The findings of this research contribute insights and advancements to treatment planning and decision-making processes in the field of endodontics, providing promising avenues for improving the management of endodontic treatments and achieving better treatment outcomes.
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