Anatomical recognition artificial intelligence for identifying the recurrent laryngeal nerve during endoscopic thyroid surgery: A single‐center feasibility study
The AI model showed high recognition accuracy of the RLN and trachea. This method holds potential for assisting in future cervical gasless endoscopic surgeries.
Authors
- Kazuyuki Hayashi (ORCID: https://orcid.org/0000-0001-9299-680X)
- Takeshi Shinozaki (ORCID: https://orcid.org/0000-0001-6762-8490)
- Ryuichi Hayashi (ORCID: https://orcid.org/0000-0002-4889-4539)
- Kazuto Matsuura (ORCID: https://orcid.org/0000-0001-9671-7230)
- Hirotaka Tanaka (ORCID: https://orcid.org/0000-0002-5322-5479)
- Tateo Ogane
- Toshifumi Tomioka (ORCID: https://orcid.org/0000-0003-0406-3976)
- Yukio Nishiya (ORCID: https://orcid.org/0000-0003-1666-3442)
- Wataru Okano (ORCID: https://orcid.org/0000-0003-3915-4229)
- Yoshiaki Kinebuchi
Institutions
- Jikei University School of Medicine (JP)
- National Cancer Center Hospital East (JP)
Publication Details
- Journal
- Laryngoscope Investigative Otolaryngology
- Published
- 2024-12-01
- DOI
- https://doi.org/10.1002/lio2.70049
- Citations
- 5
- Primary Topic
- Thyroid and Parathyroid Surgery
- Type
- article
- Field-Weighted Citation Impact
- 2.86
Funders
- Japan Society for the Promotion of Science