ArticleLaryngoscope investigative otolaryngology2024
Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single-center feasibility study.
Article in Laryngoscope investigative otolaryngology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Opinions and attitudes toward artificial intelligence among operating room nurses: a descriptive meta-analysis based on the comparative studies of the different opinions.Frontiers in artificial intelligence · 2025Pooled it
- Role of artificial intelligence in head and neck surgery - A narrative review.Turkish journal of surgery · 2026Article
- Deep learning-based renal artery segmentation and angle estimation for registration of endoscopic images and 3D models in robot-assisted partial nephrectomy.International journal of computer assisted radiology and surgery · 2026Article
- Integrating Robotic Bilateral Axillo-Breast Approach Thyroidectomy with Molecular Diagnostics and Artificial Intelligence in Thyroid Cancer Care.Biomolecules & therapeutics · 2026Review
- Artificial intelligence in otorhinolaryngology: current trends and application areas.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Review
- Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single-center feasibility study.Laryngoscope investigative otolaryngology · 2024Article
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Authors and funding
10 authors.
Funding
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Abstract
Background: We investigate the feasibility of using artificial intelligence (AI) to identify the recurrent laryngeal nerve (RLN) during endoscopic thyroid surgery and evaluated its accuracy. Methods: In this retrospective study, we develop an AI model using a dataset of endoscopic thyroid surgery videos, including hemithyroidectomy procedures performed between April 2019 and September 2023 at the National Cancer Center Hospital East, Chiba, Japan. Semantic segmentation deep learning methods were applied to analyze the endoscopic thyroid surgery videos. Results: Forty endoscopic thyroid surgery videos, all in high definition or better quality, were analyzed. The Dice values were 0.351, 0.568, and 0.746 for the inferior thyroid artery, RLN, and trachea, respectively. Data augmentation was performed by cropping, standardizing, and resizing to reduce false positives and improve accuracy. Conclusions: The AI model showed high recognition accuracy of the RLN and trachea. This method holds potential for assisting in future cervical gasless endoscopic surgeries.
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