Cognitive Computing Approaches for Assessing Mandibular Third Molar Impaction: A Systematic Review

Context: Mandibular third molar impaction presents significant diagnostic and surgical challenges. Artificial intelligence, particularly deep learning approaches, has emerged as a promising tool for classification, risk assessment, and surgical planning in oral and maxillofacial surgery. Objective: To systematically evaluate the diagnostic performance and clinical applicability of cognitive computing techniques for assessing mandibular third molar impaction and associated surgical complexities. Evidence Acquisition: A systematic literature search was conducted in accordance with PRISMA 2020 guidelines using PubMed, Scopus, Google Scholar Web of Science, and IEEE Xplore databases for studies published up to January 2025. Search terms included combinations of "third molar," "wisdom tooth," "impaction," "artificial intelligence," "deep learning," "radiograph," and "convolutional neural networks." Two reviewers independently screened and extracted data. Risk of bias was assessed using adapted Joanna Briggs Institute (JBI) quality assessment tools. Results: Fifteen studies met inclusion criteria, involving primarily convolutional neural networks (CNNs), support vector machines (SVMs), and ensemble classifiers. Most studies utilised panoramic radiographs, whilst some incorporated cone-beam computed tomography (CBCT) or clinical metadata. AI systems demonstrated diagnostic accuracies ranging from 78.91% to 99.0% for impaction classification, and 72.32% to 99.0% for mandibular canal proximity assessment. Multiple studies showed that AI models outperformed dental students and general practitioners, though performance varied across different architectural approaches and training datasets. Several models demonstrated potential for predicting extraction time (mean absolute error < 3 minutes) and postoperative complications (accuracy up to 98%). Limitations: Limited generalisability due to single-institution datasets, variable dataset sizes, and heterogeneous methodological approaches. Publication bias and selective outcome reporting were not comprehensively assessed. Conclusions: Deep learning-based artificial intelligence demonstrates considerable promise for objective classification of mandibular third molar impaction, proximity assessment with the mandibular canal, and prediction of surgical complexity. However, further development using large multi-institutional datasets, adherence to standardised diagnostic test protocols, and prospective clinical validation are essential before integration into routine clinical practice. Collaborative research involving oral radiologists, clinicians, and computer scientists is required to optimise AI model development and ensure clinical applicability.