ArticleFrontiers in dental medicine2026
Impacted third molars and their influence on second molar pathologies: radiological patterns, statistical analysis, and emerging AI perspectives.
Article in Frontiers in dental medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background/objectives: Impacted mandibular third molars (M3) often contribute to pathologies of adjacent second molars (M2), including periodontal disease, infections, and caries. This study evaluated how the position of impacted lower wisdom teeth influences M2 pathologies and explored the potential of AI-assisted radiographic analysis. Methods: A retrospective review of 134 orthopantomograms with impacted or semi-impacted M3s was conducted to assess disease prevalence in both M2 and M3. AI-based image processing tools were applied experimentally to evaluate diagnostic accuracy and limitations. Results: The findings are organized into three primary sections: the distribution of healthy and diseased teeth for each of the two molars, the types of diseases detected in the second molar, and the overall frequency of pathologies associated with both the second molar and the wisdom tooth. Distal caries of M2 were the most common pathology (59.7%), followed by periodontal bone loss (32.1%) and external root resorption (11.9%). Coronal caries of M3 were observed in 47.0% of cases. Mesioangular and deeper impactions showed the strongest associations with M2 disease. Artificial Intelligence(AI)-assisted image analysis tools were utilized in this study to support the evaluation of dental radiographs. Conclusions: Distal caries and periodontal involvement are mostly determined by impaction type and depth, particularly mesioangular and deeper impactions (Pell & Gregory IIB, IIIB). The finding that M2 distal caries is the most common pathology, affecting nearly 60% of cases, and that M2 caries significantly increase the likelihood of M3 caries reinforces the idea that identifying high-risk patterns, such as mesioangular and deeper impactions, enables clinicians to anticipate associated pathologies and customise management strategies effectively. While current AI tools face challenges in precise dental diagnosis, their integration shows promise for improving radiographic interpretation and supporting clinical decision-making.
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