Laryngoscope investigative otolaryngology · 2024
Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single-center feasibility study.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
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Nishiya Yukio, Matsuura Kazuto, Ogane Tateo, Hayashi Kazuyuki, Kinebuchi Yumi, Tanaka Hirotaka, Okano Wataru, Tomioka Toshifumi, Shinozaki Takeshi, Hayashi Ryuichi
Аннотация
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.
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.