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Updated: May 24, 2026

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

A Postoperative Evaluation Guideline for Computer-Assisted Reconstruction of the Mandible

Published on: January 28, 2020

Postoperative computed tomography segmentation with artificial intelligence for a closed-loop workflow in

S Wu¹, P H Leung¹, K Y Li²

¹Division of Oral and Maxillofacial Surgery, Faculty of Dentistry, The University of Hong Kong, Hong Kong Special Administrative Region of China.

International Journal of Oral and Maxillofacial Surgery

|May 22, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) significantly improves postoperative CT segmentation for computer-assisted jaw reconstruction (CAJR), outperforming routine methods. This AI application enhances accuracy in maxillofacial surgery workflows.

Keywords:

Artificial intelligence Computed tomography Computer-assisted surgery Image processing Maxillofacial surgery Reconstructive surgery

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Area of Science:

Medical Imaging
Artificial Intelligence in Surgery
Maxillofacial Surgery

Background:

Postoperative CT segmentation is crucial for computer-assisted jaw reconstruction (CAJR) but faces challenges like anatomical variations and metallic artifacts.
Current clinical routine segmentation methods serve as a benchmark but are less efficient.

Purpose of the Study:

To evaluate the performance of artificial intelligence (AI)-enabled segmentation on postoperative CT scans.
To compare AI segmentation outcomes against clinical routine methods in CAJR.

Main Methods:

AI-enabled segmentation was applied to postoperative CT scans.
Outcomes were compared to clinical routine segmentation using Dice Similarity Coefficient (DCE), intersection over union, and Hausdorff distances.
Statistical analyses identified correlations between segmentation performance and confounding factors.

Main Results:

AI-enabled segmentation significantly outperformed clinical routine methods across all evaluation metrics (P < 0.013).
AI achieved high DCE values for the upper skull (95.21% ± 2.07%) and mandible (94.28% ± 3.03%).
Reconstructed mandibles showed slightly poorer AI segmentation outcomes, with deep circumflex iliac artery flaps performing better than fibula free flaps (P < 0.025).

Conclusions:

AI-enabled segmentation demonstrates superior performance in postoperative CT scans for CAJR.
AI integration into the CAJR workflow is supported by these findings, improving accuracy and efficiency.
Further investigation into specific flap types and AI segmentation is warranted.