Accuracy of artificial intelligence-based segmentation in maxillofacial structures: a systematic review

Abstract

OBJECTIVE: The aim of this review was to evaluate the accuracy of artificial intelligence (AI) in the segmentation of teeth, jawbone (maxilla, mandible with temporomandibular joint), and mandibular (inferior alveolar) canal in CBCT and CT scans. MATERIALS AND METHODS: Articles were retrieved from MEDLINE, Cochrane CENTRAL, IEEE Xplore, and Google Scholar. Eligible studies were analyzed thematically, and their quality was appraised using the JBI checklist for diagnostic test accuracy studies. Meta-analysis was conducted for key performance metrics, including Dice Similarity Coefficient (DSC) and Average Surface Distance (ASD). RESULTS: A total of 767 non-duplicate articles were identified, and 30 studies were included in the review. Of these, 27 employed deep-learning models, while 3 utilized classical machine-learning approaches. The pooled DSC for mandible segmentation was 0.94 (95% CI: 0.91-0.98), mandibular canal segmentation was 0.694 (95% CI: 0.551-0.838), maxilla segmentation was 0.907 (95% CI: 0.867-0.948), and teeth segmentation was 0.925 (95% CI: 0.891-0.959). Pooled ASD values were 0.534 mm (95% CI: 0.366-0.703) for the mandibular canal, 0.468 mm (95% CI: 0.295-0.641) for the maxilla, and 0.189 mm (95% CI: 0.043-0.335) for teeth. Other metrics, such as sensitivity and precision, were variably reported, with sensitivity exceeding 90% across studies. CONCLUSION: AI-based segmentation, particularly using deep-learning models, demonstrates high accuracy in the segmentation of dental and maxillofacial structures, comparable to expert manual segmentation. The integration of AI into clinical workflows offers not only accuracy but also substantial time savings, positioning it as a promising tool for automated dental imaging.

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