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Artificial Intelligence-enabled Automatic Computed Tomography Segmentation for Craniomaxillofacial Surgery Using a

Songying Wu1, Pui Hang Leung1, Andy Wai Kan Yeung2

  • 1From the Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong.

Plastic and Reconstructive Surgery. Global Open
|March 4, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) offers accurate skull CT segmentation for craniomaxillofacial surgery, outperforming traditional methods in critical areas. This AI tool enhances surgical planning by improving segmentation accuracy and efficiency.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Surgery
  • Craniomaxillofacial Surgery

Background:

  • Skull computed tomography (CT) segmentation is crucial for computer-assisted craniomaxillofacial surgery.
  • Traditional threshold-based segmentation is time-consuming and often suboptimal, especially in complex anatomical regions.
  • This study assesses an AI-enabled automatic segmentation method for skull CT scans.

Purpose of the Study:

  • To evaluate the performance of an AI-enabled automatic skull CT segmentation method.
  • To compare AI segmentation with the clinical routine method.
  • To investigate clinical factors influencing AI segmentation accuracy.

Main Methods:

  • Quantitative and qualitative assessment of segmentation outcomes from 44 preoperative skull CT scans.
  • Comparison between AI-enabled and clinical routine segmentation methods.
  • Analysis of factors including occlusal contact, metallic artifacts, bone involvement, slice increment, and pixel size.

Main Results:

  • AI segmentation achieved comparable accuracy for the upper skull (92.19% vs. 91.72%) but outperformed clinical routine segmentation for the mandible (94.81% vs. 91.77%).
  • AI demonstrated superior segmentation in critical areas like the anterior maxillary wall and temporomandibular joint.
  • Smaller slice increments and pixel sizes improved AI accuracy, while occlusal contact negatively impacted mandibular segmentation.

Conclusions:

  • AI-enabled skull CT segmentation is comparable to clinical routine methods, with enhanced performance in challenging anatomical sites.
  • The AI method shows potential for eliminating segmentation issues caused by metallic artifacts.
  • This validated AI model can be integrated into computer-assisted craniomaxillofacial surgery workflows.