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Accuracy of artificial intelligence-based segmentation in maxillofacial structures: a systematic review.
Manea Alahmari1, Maram Alahmari2, Abdulmajeed Almuaddi1
1College of Dentistry, King Khalid University, Abha, Saudi Arabia.
BMC Oral Health
|March 7, 2025
Summary
Artificial intelligence (AI) shows high accuracy in segmenting dental and maxillofacial structures from CBCT and CT scans. Deep-learning models offer precise segmentation comparable to experts, saving time in clinical workflows.
Area of Science:
- Radiology
- Medical Imaging
- Artificial Intelligence
Background:
- Accurate segmentation of dental and maxillofacial structures is crucial for diagnosis and treatment planning.
- Manual segmentation is time-consuming and prone to inter-observer variability.
- Artificial intelligence (AI) offers potential for automated and accurate segmentation.
Purpose of the Study:
- To evaluate the accuracy of AI in segmenting teeth, jawbone (maxilla, mandible, temporomandibular joint), and mandibular canal in CBCT and CT scans.
- To compare AI-based segmentation performance with established metrics.
Main Methods:
- A systematic review and meta-analysis of studies evaluating AI for dental and maxillofacial segmentation.
- Searched databases including MEDLINE, Cochrane CENTRAL, IEEE Xplore, and Google Scholar.
- Analyzed 30 studies, primarily using deep-learning models, assessing metrics like Dice Similarity Coefficient (DSC) and Average Surface Distance (ASD).
Main Results:
- AI demonstrated high accuracy in segmenting mandible (DSC: 0.94), maxilla (DSC: 0.907), and teeth (DSC: 0.925).
- Mandibular canal segmentation achieved a pooled DSC of 0.694 with an ASD of 0.534 mm.
- Sensitivity exceeded 90% across most studies, indicating robust performance.
Conclusions:
- AI-based segmentation, especially deep learning, is highly accurate for dental and maxillofacial structures.
- AI segmentation accuracy is comparable to expert manual segmentation.
- AI integration promises significant time savings and improved efficiency in dental imaging workflows.
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