Abstract
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.
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Data availability
The data that support the findings of this study are openly available in Kaggle at https://doi.org/10.34740/kaggle/dsv/1201463023. A detailed description of the data files and structure is provided in the Data Record section of this paper.
Code availability
The code for the models related to the technical validation of this dataset is publicly available at: https://github.com/AriaCui/mmsegmentation?tab=readme-ov-file#.
References
Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3), 209–249 (2021).
Boucai, L., Zafereo, M. & Cabanillas, M. E. Thyroid Cancer: A Review. Jama 331(5), 425–435 (2024).
Chen, D. W. et al. Thyroid cancer. Lancet 401(10387), 1531–1544 (2023).
Cao, W. et al. Socioeconomic inequalities in cancer incidence and mortality: An analysis of GLOBOCAN 2022. Chin Med J (Engl) 137(12), 1407–1413 (2024).
Cheng, X. et al. Progress in gasless endoscopic thyroidectomy. Front Endocrinol (Lausanne) 15, 1466837 (2024).
Yang, Y. et al. Endoscopic thyroidectomy for differentiated thyroid cancer. ScientificWorldJournal 2012, 456807 (2012).
Dionigi, G. et al. Neuromonitoring in endoscopic and robotic thyroidectomy. Updates Surg 69(2), 171–179 (2017).
Liu, Y. C. et al. Effectiveness of the recurrent laryngeal nerve monitoring during endoscopic thyroid surgery: systematic review and meta-analysis. Int J Surg 109(7), 2070–2081 (2023).
Sun, H. & Tian, W. Chinese guidelines on intraoperative neuromonitoring in thyroid and parathyroid surgery (2023 edition). Gland Surg 12(8), 1031–1049 (2023).
den Boer, R. B. et al. Deep learning-based recognition of key anatomical structures during robot-assisted minimally invasive esophagectomy. Surg Endosc 37(7), 5164–5175 (2023).
Casella, A. et al. NephCNN: A deep-learning framework for vessel segmentation in nephrectomy laparoscopic videos. in 2020 25th International Conference on Pattern Recognition (ICPR). (2021).
Kitaguchi, D. et al. Real-time vascular anatomical image navigation for laparoscopic surgery: experimental study. Surg Endosc 36(8), 6105–6112 (2022).
Ryu, S. et al. Artificial intelligence-enhanced navigation for nerve recognition and surgical education in laparoscopic colorectal surgery. Surg Endosc 39(2), 1388–1396 (2025).
Ryu, S. et al. Laparoscopic Colorectal Surgery with Anatomical Recognition with Artificial Intelligence Assistance for Nerves and Dissection Layers. Ann Surg Oncol 31(3), 1690–1691 (2024).
Sengun, B. et al. Utilization of artificial intelligence in minimally invasive right adrenalectomy: recognition of anatomical landmarks with deep learning. Acta Chir Belg 124(6), 492–498 (2024).
Gon Park, S. et al. Deep Learning Model for Real-time Semantic Segmentation During Intraoperative Robotic Prostatectomy. European Urology Open Science 62, 47–53 (2024).
Mao, Z. et al. PitSurgRT: real-time localization of critical anatomical structures in endoscopic pituitary surgery. Int J Comput Assist Radiol Surg 19(6), 1053–1060 (2024).
Ríos, M. S. et al. Cholec80-CVS: An open dataset with an evaluation of Strasberg’s critical view of safety for AI. Sci Data 10(1), 194 (2023).
Kitaguchi, D. et al. Artificial intelligence for the recognition of key anatomical structures in laparoscopic colorectal surgery. British Journal of Surgery 110(10), 1355–1358 (2023).
Carstens, M. et al. The Dresden Surgical Anatomy Dataset for Abdominal Organ Segmentation in Surgical Data Science. Scientific Data. 10(1) (2023).
Fehling, M. K. et al. Fully automatic segmentation of glottis and vocal folds in endoscopic laryngeal high-speed videos using a deep Convolutional LSTM Network. PLoS One 15(2), e0227791 (2020).
Gong, J. et al. Using deep learning to identify the recurrent laryngeal nerve during thyroidectomy. Scientific Reports 11(1), 14306 (2021).
Zheng, H. ThyRLN-PUMCH. Kaggle. https://doi.org/10.34740/kaggle/dsv/12014630 (2025).
Chen, L.-C. et al. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation. in European Conference on Computer Vision. (2018).
Cheng, B. et al. Masked-attention Mask Transformer for Universal Image Segmentation. https://doi.org/10.48550/arXiv.2112.01527 arXiv:2112.01527 (2021).
Acknowledgements
This work was supported by National High Level Hospital Clinical Research Funding, No. 2022-PUMCH-A-052 and No. 2022-PUMCH-B-003.
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Data Collection, H.Z., R.C., J.G., S.H., Q.L., G.C.; Parameter and Model Adjustment, H.Z.; Original Draft Preparation, H.Z., R.C., J.G., Q.Y.; Review and Editing, H.Z., R.C., J.G., Q.Y., S.Y.; Supervision, S.H., Q.L.
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Zheng, H., Cui, R., Gao, J. et al. A High-Quality Endoscopic Image Dataset with Annotated Recurrent Laryngeal Nerve for AI-Assisted Thyroid Surgery. Sci Data (2026). https://doi.org/10.1038/s41597-026-06961-6
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DOI: https://doi.org/10.1038/s41597-026-06961-6
