Efficacy of Ai Enabled Software in Automatic Segmentation for Orthognathic Surgery

Area of Science:

• Medical Imaging
• Artificial Intelligence in Surgery
• 3D Modeling

Background:

• Orthognathic surgery requires precise preoperative planning and postoperative evaluation.
• Manual segmentation and alignment of imaging data are time-consuming tasks.

Purpose of the Study:

• To investigate the efficiency and accuracy of an AI-based deep learning platform for maxillofacial region segmentation and dental occlusion data alignment.
• To automate key tasks in orthognathic surgery workflows.

Main Methods:

• Retrospective collection of 20 preoperative CT scans from patients undergoing bimaxillary orthognathic surgery.
• Utilizing an AI platform for automatic identification and segmentation of anatomical structures.
• Aligning STL files from dental scans with DICOM files from CT scans.

Main Results:

• AI software processing time averaged approximately 6 minutes and 59 seconds.
• Average operator segmentation time was 38.9 seconds.
• The AI platform achieved precise alignment of STL and DICOM files without manual adjustments.

Conclusions:

• The AI system substantially reduced segmentation times and demonstrated high accuracy.
• The AI platform is a valuable tool for enhancing orthognathic surgery workflows.
• Further validation on larger, diverse datasets is recommended.