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Estimating Reference Bony Shape Models for Orthognathic Surgical Planning Using 3D Point-Cloud Deep Learning.

Deqiang Xiao, Chunfeng Lian, Hannah Deng

    IEEE Journal of Biomedical and Health Informatics
    |January 26, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a deep learning framework for automatically estimating facial bone shapes in orthognathic surgery planning. This AI-driven approach enhances accuracy and efficiency by reducing variability in surgical planning.

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    Area of Science:

    • Medical Imaging
    • Computer-Aided Surgery
    • Artificial Intelligence in Medicine

    Background:

    • Orthognathic surgery outcomes are critically dependent on precise surgical planning.
    • Manual facial bone shape estimation is subjective and varies with experience.
    • Automated methods can improve accuracy and efficiency in surgical planning.

    Purpose of the Study:

    • To develop an end-to-end deep learning framework for automatic estimation of patient-specific reference facial bone shapes.
    • To reduce experience-dependent variability in orthognathic surgical planning.
    • To improve the accuracy and efficiency of surgical planning for orthognathic deformities.

    Main Methods:

    • A point-cloud network was employed to learn a vertex-wise deformation field from patient-specific deformed bony shapes.
    • A novel simulation strategy was developed to synthesize diverse datasets of deformed bones for network training.
    • The framework corrects deformed bony shapes to generate patient-specific reference bony surface models.

    Main Results:

    • The proposed framework successfully estimates realistic reference bony shape models for diverse orthognathic deformities.
    • Evaluation on synthetic and real patient data demonstrated superior performance compared to existing methods.
    • The deep learning approach consistently outperformed several established deep point-cloud networks.

    Conclusions:

    • The end-to-end deep learning framework offers a robust solution for estimating patient-specific reference facial bone shapes.
    • This geometric deep learning approach shows significant potential for enhancing clinical workflows in orthognathic surgery.
    • The method promises to improve the precision and efficiency of surgical planning for patients with facial deformities.