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Related Concept Videos

  • Information And Computing Sciences
  • Artificial Intelligence
  • Natural Language Processing
  • Anatomical Recognition Artificial Intelligence For Identifying The Recurrent Laryngeal Nerve During Endoscopic Thyroid Surgery: A Single-center Feasibility Study.
  • Information And Computing Sciences
  • Artificial Intelligence
  • Natural Language Processing
  • Anatomical Recognition Artificial Intelligence For Identifying The Recurrent Laryngeal Nerve During Endoscopic Thyroid Surgery: A Single-center Feasibility Study.

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    Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single-center feasibility study.

    Yukio Nishiya1,2, Kazuto Matsuura1, Tateo Ogane3

    • 1Department of Head and Neck Surgery National Cancer Center Hospital East Chiba Japan.

    Laryngoscope Investigative Otolaryngology
    |December 6, 2024

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    Artificial intelligence (AI) accurately identifies the recurrent laryngeal nerve (RLN) during endoscopic thyroid surgery. This AI model shows promise for assisting in future cervical gasless endoscopic procedures.

    Keywords:
    artificial intelligencedeep learningendoscopic thyroid surgeryrecurrent laryngeal nervethyroidectomy

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    Area of Science:

    • Surgical Innovation
    • Medical Imaging
    • Artificial Intelligence in Medicine

    Background:

    • Endoscopic thyroid surgery presents challenges in identifying critical anatomical structures.
    • Accurate identification of the recurrent laryngeal nerve (RLN) is crucial for preventing vocal cord dysfunction.

    Purpose of the Study:

    • To assess the feasibility and accuracy of an AI model for identifying the recurrent laryngeal nerve (RLN) during endoscopic thyroid surgery.
    • To evaluate the AI model's performance in a clinical setting.

    Main Methods:

    • A retrospective study utilizing a dataset of endoscopic thyroid surgery videos (hemithyroidectomy).
    • Development of an AI model employing semantic segmentation deep learning techniques.
    • Analysis of 40 high-definition endoscopic videos with data augmentation strategies.

    Main Results:

    • The AI model achieved Dice values of 0.568 for the recurrent laryngeal nerve (RLN) and 0.746 for the trachea.
    • High recognition accuracy was observed for both the RLN and trachea.
    • Data augmentation techniques helped reduce false positives and enhance accuracy.

    Conclusions:

    • The developed AI model demonstrates high accuracy in identifying the recurrent laryngeal nerve (RLN) and trachea during endoscopic thyroid surgery.
    • This AI approach holds significant potential to aid surgeons in future cervical gasless endoscopic procedures.