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Related Experiment Video

Updated: Jul 10, 2026

Three-Dimensional Reconstruction of Orbital Fractures
08:18

Three-Dimensional Reconstruction of Orbital Fractures

Published on: May 16, 2025

Fully automated AI-based digital workflow for orbital fracture mirroring: A multicenter approach.

Sorana Eftimie1, Michel Beyer2, Ashkan Rashad3

  • 1Department of Maxillofacial Surgery and Radiology, Iuliu Hațieganu University of Medicine and Pharmacy, Cluj-Napoca, 400347, Romania.

Journal of Cranio-Maxillo-Facial Surgery : Official Publication of the European Association for Cranio-Maxillo-Facial Surgery
|July 8, 2026
PubMed
Summary

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Head & face medicine·2026

An AI-driven workflow automates orbital segmentation and mirroring for reconstructive surgery. This approach enhances efficiency and reproducibility, improving patient outcomes in orbital reconstruction.

Area of Science:

  • Medical imaging
  • Artificial intelligence in medicine
  • Surgical planning

Background:

  • Orbital reconstruction demands precise anatomical restoration for function and aesthetics.
  • Current virtual surgical planning relies on manual, operator-dependent segmentation and mirroring.

Purpose of the Study:

  • To develop and validate a fully automated, AI-based workflow for orbital segmentation and mirroring.
  • To improve efficiency and reproducibility in orbital reconstruction surgery.

Main Methods:

  • Trained a 3D nnU-Net model on 502 multi-institutional CT scans for automated orbital segmentation.
  • Employed Principal Component Analysis and Iterative Closest Point registration for algorithmic mirroring.
  • Benchmarked automated results against manual segmentation by clinical experts using Dice Similarity Coefficient, Mean Surface Distance, and Hausdorff Distance.
Keywords:
Artificial intelligenceComputer simulationImage processingOrbital fracturesVirtual surgical planning

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Last Updated: Jul 10, 2026

Three-Dimensional Reconstruction of Orbital Fractures
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Published on: May 16, 2025

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Main Results:

  • AI-driven segmentation achieved high accuracy (mean Dice Similarity Coefficient of 0.936), particularly in non-fractured orbits (up to 0.941).
  • Automated mirroring demonstrated minimal angular deviation from manual planes (fractured: 0.95° ± 0.65°; non-fractured: 0.72° ± 0.53°).
  • Consistent performance across international datasets (Germany, Romania, USA) indicates robustness.

Conclusions:

  • The automated AI workflow provides robust and clinically applicable segmentation and mirroring for orbital reconstruction.
  • This technology enables standardized, efficient, and reproducible surgical planning.
  • Potential to significantly improve functional and aesthetic outcomes in patients undergoing orbital reconstruction.