Scholarlink

<p>The integration of artificial intelligence (AI) into surgical navigation represents a pivotal advancement in modern operative medicine. In endoscopic thyroidectomy, safeguarding the recurrent laryngeal nerve (RLN) is of critical importance due to its vulnerability to iatrogenic injury, which affects 3–8% of cases and can lead to serious complications such as vocal cord paralysis. However, existing intraoperative nerve monitoring (IONM) technologies are limited by high costs, operator dependence, and discontinuous signal acquisition. To address the lack of large-scale, annotated datasets essential for training robust deep learning models in real-world surgical settings, we present ThyRLN-PUMCH, the first comprehensive <i>in vivo</i> dataset dedicated to RLN identification in endoscopic thyroid surgery. This dataset comprises 18,178 pixel-level annotated frames from 28 clinically diverse surgical cases. Annotations were performed and validated by board-certified endocrine surgeons through a multi-stage quality control process. We benchmarked two segmentation models to verify their practicability and proved the dataset’s capacity to support high-precision RLN segmentation tasks. ThyRLN-PUMCH fills a critical gap in AI assisted head and neck surgery by offering temporally continuous, clinically representative images and annotations. It provides a robust foundation for developing AI-based intraoperative navigation tools aimed at enhancing surgical safety, education, and efficiency in head and neck surgery.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A High-Quality Endoscopic Image Dataset with Annotated Recurrent Laryngeal Nerve for AI-Assisted Thyroid Surgery

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月3日

查看全文
  • Huaijin Zheng,
  • Ruohan Cui,
  • Junyi Gao,
  • Qi Yan,
  • Sen Yang,
  • Quan Liao,
  • Surong Hua

摘要

The integration of artificial intelligence (AI) into surgical navigation represents a pivotal advancement in modern operative medicine. In endoscopic thyroidectomy, safeguarding the recurrent laryngeal nerve (RLN) is of critical importance due to its vulnerability to iatrogenic injury, which affects 3–8% of cases and can lead to serious complications such as vocal cord paralysis. However, existing intraoperative nerve monitoring (IONM) technologies are limited by high costs, operator dependence, and discontinuous signal acquisition. To address the lack of large-scale, annotated datasets essential for training robust deep learning models in real-world surgical settings, we present ThyRLN-PUMCH, the first comprehensive in vivo dataset dedicated to RLN identification in endoscopic thyroid surgery. This dataset comprises 18,178 pixel-level annotated frames from 28 clinically diverse surgical cases. Annotations were performed and validated by board-certified endocrine surgeons through a multi-stage quality control process. We benchmarked two segmentation models to verify their practicability and proved the dataset’s capacity to support high-precision RLN segmentation tasks. ThyRLN-PUMCH fills a critical gap in AI assisted head and neck surgery by offering temporally continuous, clinically representative images and annotations. It provides a robust foundation for developing AI-based intraoperative navigation tools aimed at enhancing surgical safety, education, and efficiency in head and neck surgery.