Deep Learning in Third Molar Management
Articles, Reviews and Lectures
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11m
This source presents a comprehensive systematic review and meta-analysis evaluating the effectiveness of deep learning (DL) models in assisting with clinical decisions related to mandibular third molars (MM3). The research analyzes a significant number of studies and dental images, assessing DL's capability in tasks such as identifying the spatial relationship between MM3 and the inferior alveolar nerve (IAN), predicting eruption, and evaluating extraction complexity. Findings indicate that DL demonstrates high diagnostic accuracy, often comparable to or exceeding human experts, particularly in anatomical identification using medical imaging like panoramic radiographs (OPGs) and cone beam CT (CBCT). The study concludes that image-based DL tools hold substantial value for enhancing clinical management and diagnostic precision in oral and maxillofacial surgery, although challenges related to data quality and standardization remain.
1.Zhao, C. et al. The Utility Of Deep Learning Model in Clinical Treatment Decision-making of Mandibular Third Molar: A Systematic Review and Meta-analysis. J. Évid.-Based Dent. Pr.
102164 (2025) doi:10.1016/j.jebdp.2025.102164.
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