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

IF 1.6 4区医学 Q2 OTORHINOLARYNGOLOGY

Laryngoscope Investigative Otolaryngology Pub Date : 2024-12-05 DOI:10.1002/lio2.70049

Yukio Nishiya MD, PhD, Kazuto Matsuura MD, PhD, Tateo Ogane, Kazuyuki Hayashi MEng, Yumi Kinebuchi MBA, Hirotaka Tanaka MEng, Wataru Okano MD, PhD, Toshifumi Tomioka MD, PhD, Takeshi Shinozaki MD, PhD, Ryuichi Hayashi MD

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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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解剖识别人工智能在内镜甲状腺手术中识别喉返神经：单中心可行性研究。

背景：我们探讨了在内镜甲状腺手术中使用人工智能（AI）识别喉返神经（RLN）的可行性，并评估其准确性。方法：在这项回顾性研究中，我们使用内镜甲状腺手术视频数据集开发了一个人工智能模型，其中包括2019年4月至2023年9月在日本千叶国立癌症中心东医院进行的甲状腺切除术。应用语义分割深度学习方法对内镜下甲状腺手术视频进行分析。结果：对40个甲状腺内镜手术视频进行分析，均为高清晰度或较好质量。甲状腺下动脉、RLN、气管的Dice值分别为0.351、0.568、0.746。通过裁剪、标准化和调整大小来进行数据增强，以减少误报并提高准确性。结论：人工智能模型对RLN和气管具有较高的识别准确率。这种方法有潜力协助未来的颈椎无气腹内窥镜手术。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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