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CLPNet: Cleft Lip and Palate Surgery Support With Deep Learning.

Yizhou Li, Junhao Cheng, Hongxiang Mei

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |January 18, 2020
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    This study introduces a deep learning-based robotic surgery assistant to improve cleft lip and palate repair outcomes. The technology aids in precise surgical marker and incision placement, reducing the technical demands for surgeons.

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    Area of Science:

    • Oral and Maxillofacial Surgery
    • Medical Robotics
    • Artificial Intelligence in Healthcare

    Background:

    • Cleft lip and palate (CLP) is a common congenital oral and maxillofacial malformation.
    • Successful CLP repair relies heavily on accurate surgical marker and incision design.
    • Limited medical resources in some areas hinder optimal surgical outcomes.

    Purpose of the Study:

    • To develop a novel robotic surgery assistant technology using deep learning for CLP repair.
    • To reduce the technical threshold and enhance the effectiveness of CLP repair surgery.
    • To establish a robust dataset for training surgical marker and incision localization models.

    Main Methods:

    • Creation of a novel, robust dataset for cleft lip and palate cases.
    • Implementation of a deep learning model utilizing Hourglass architecture and residual learning.
    • Development of two novel block designs for improved generalization and reduced model complexity.

    Main Results:

    • The developed deep learning models achieved superior performance in locating surgical markers and incisions for CLP repair.
    • The models demonstrated strong superiority and adaptability compared to other facial feature extraction methods.
    • The proposed system shows potential for efficient application in surgical settings.

    Conclusions:

    • The novel robotic surgery assistant technology based on deep learning shows significant promise for improving cleft lip and palate repair.
    • The established dataset and advanced deep learning models offer a valuable tool for surgical planning and execution.
    • This technology can help standardize and improve the quality of care for patients with cleft lip and palate, particularly in resource-limited settings.