Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single‐center feasibility study
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 sur...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | Laryngoscope Investigative Otolaryngology |
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| Online Access: | https://doi.org/10.1002/lio2.70049 |
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| author | Yukio Nishiya Kazuto Matsuura Tateo Ogane Kazuyuki Hayashi Yumi Kinebuchi Hirotaka Tanaka Wataru Okano Toshifumi Tomioka Takeshi Shinozaki Ryuichi Hayashi |
| author_facet | Yukio Nishiya Kazuto Matsuura Tateo Ogane Kazuyuki Hayashi Yumi Kinebuchi Hirotaka Tanaka Wataru Okano Toshifumi Tomioka Takeshi Shinozaki Ryuichi Hayashi |
| author_sort | Yukio Nishiya |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-9780275e5bf24b6bb71304b40ceb232e |
| institution | OA Journals |
| issn | 2378-8038 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | Laryngoscope Investigative Otolaryngology |
| spelling | doaj-art-9780275e5bf24b6bb71304b40ceb232e2025-08-20T02:31:36ZengWileyLaryngoscope Investigative Otolaryngology2378-80382024-12-0196n/an/a10.1002/lio2.70049Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single‐center feasibility studyYukio Nishiya0Kazuto Matsuura1Tateo Ogane2Kazuyuki Hayashi3Yumi Kinebuchi4Hirotaka Tanaka5Wataru Okano6Toshifumi Tomioka7Takeshi Shinozaki8Ryuichi Hayashi9Department of Head and Neck Surgery National Cancer Center Hospital East Chiba JapanDepartment of Head and Neck Surgery National Cancer Center Hospital East Chiba JapanDepartment of Medical Device Innovation National Cancer Center Hospital East Chiba JapanDepartment of Medical Device Innovation National Cancer Center Hospital East Chiba JapanDepartment of Medical Device Innovation National Cancer Center Hospital East Chiba JapanCenter for Promotion of Translational Research, National Cancer Center Tokyo JapanDepartment of Head and Neck Surgery National Cancer Center Hospital East Chiba JapanDepartment of Head and Neck Surgery National Cancer Center Hospital East Chiba JapanDepartment of Head and Neck Surgery National Cancer Center Hospital East Chiba JapanDepartment of Head and Neck Surgery National Cancer Center Hospital East Chiba JapanAbstract 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.https://doi.org/10.1002/lio2.70049artificial intelligencedeep learningendoscopic thyroid surgeryrecurrent laryngeal nervethyroidectomy |
| spellingShingle | Yukio Nishiya Kazuto Matsuura Tateo Ogane Kazuyuki Hayashi Yumi Kinebuchi Hirotaka Tanaka Wataru Okano Toshifumi Tomioka Takeshi Shinozaki Ryuichi Hayashi Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single‐center feasibility study Laryngoscope Investigative Otolaryngology artificial intelligence deep learning endoscopic thyroid surgery recurrent laryngeal nerve thyroidectomy |
| title | Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single‐center feasibility study |
| title_full | Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single‐center feasibility study |
| title_fullStr | Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single‐center feasibility study |
| title_full_unstemmed | Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single‐center feasibility study |
| title_short | Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single‐center feasibility study |
| title_sort | anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery a single center feasibility study |
| topic | artificial intelligence deep learning endoscopic thyroid surgery recurrent laryngeal nerve thyroidectomy |
| url | https://doi.org/10.1002/lio2.70049 |
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