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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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Automated condylar seating assessment using a deep learning-based three-step approach.

Bo Berends1,2, Shankeeth Vinayahalingam3, Frank Baan2

  • 1Department of Oral and Maxillofacial Surgery, Radboud University Medical Centre, 6500 HB, P.O. Box 9101, Nijmegen, 590, the Netherlands.

Clinical Oral Investigations
|September 3, 2024
PubMed
Summary
This summary is machine-generated.

This study developed a deep learning tool for automated condylar seating assessment in cone-beam computed tomography (CBCT) scans. The AI shows promise for improving accuracy in orthognathic surgery planning and patient outcomes.

Keywords:
Computer-assisted planningCondylar seatingCone-beam computed tomographyDeep learningDigital imagingOrthognatic surgery

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in surgery
  • Orthognathic surgery planning

Background:

  • Accurate condylar seating is crucial for reliable 3D virtual surgery planning (3D VSP) in orthognathic surgery.
  • Incorrect condylar positioning can significantly impact the accuracy of bimaxillary osteotomies.
  • Current methods for assessing condylar seating can be time-consuming and subjective.

Purpose of the Study:

  • To develop and validate a novel deep learning algorithm for automated condylar seating assessment using CBCT images.
  • To evaluate the performance of the AI-based tool in a proof-of-concept study.
  • To explore the potential of AI in enhancing the precision of orthognathic surgery.

Main Methods:

  • A dataset of 60 CBCT scans (120 condyles) was used for training and validation.
  • The AI model incorporated a segmentation module, ray-casting, and a feed-forward neural network (FFNN).
  • Fivefold cross-validation was employed to train and test the algorithm, comparing predictions against ground truth labels.

Main Results:

  • The AI model achieved an overall accuracy of 0.80.
  • Key performance metrics included a positive predictive value of 0.61, negative predictive value of 0.9, and F1-score of 0.71.
  • The model demonstrated high sensitivity (0.86) and specificity (0.78), with a mean AUC of 0.87.

Conclusions:

  • The integration of segmentation, ray-casting, and FFNN provides a viable method for automating condylar seating assessment.
  • The developed AI tool shows encouraging results and potential for improving orthognathic surgery.
  • Automated assessment can help prevent errors and enhance patient outcomes in bimaxillary osteotomies.