Scholarlink

Objective <p>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.</p> Materials and methods <p>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).</p> Results <p>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&#xa0;mm (95% CI: 0.366–0.703) for the mandibular canal, 0.468&#xa0;mm (95% CI: 0.295–0.641) for the maxilla, and 0.189&#xa0;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.</p> Conclusion <p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年3月7日

查看全文
  • Manea Alahmari,
  • Maram Alahmari,
  • Abdulmajeed Almuaddi,
  • Hossam Abdelmagyd,
  • Kumuda Rao,
  • Zaid Hamdoon,
  • Mohammed Alsaegh,
  • Nallan C. S. K. Chaitanya,
  • Shishir Shetty

摘要

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.