Abstract
Nasopharyngolaryngoscopy (NPL) is widely used to examine the nasopharyngolaryngeal anatomical sites. The quality of NPL depends on the endoscopist’s performance, and incomplete examinations may contribute to missed findings in practice. Here, we developed ENDOVISTA-ENT, an intelligent quality control system trained on NPL videos from 3,630 patients. The system can monitor anatomical coverage in real time during NPL procedures. It is not designed to detect lesions. By integrating into the existing NPL workflow, it provides endoscopists with real-time feedback on anatomical coverage, examination progress, and procedure duration. To evaluate its effect, we conducted a prospective, double-centre, randomized controlled trial registered in the Chinese Clinical Trial Registry (ChiCTR2400091245). A total of 318 patients were randomly assigned to undergo ENDOVISTA-ENT-assisted or conventional NPL examination. The primary outcome was coverage of predefined anatomical sites. Results showed that ENDOVISTA-ENT-assisted NPL examinations achieved signi6cantly higher mean anatomical coverage than conventional examinations (93.08% vs. 83.50%, P < 0.0001). Importantly, this improvement occurred without significantly increasing examination time. Subgroup analyses revealed benefits across all experience levels, particularly among junior endoscopists. These findings suggest that a real-time AI-assisted quality control system can support a more standardized NPL workflow and improve endoscopists’ procedural completeness during NPL.
Data availability
The datasets generated and analyzed in this study are not publicly available due to patient privacy and ethical constraints, but are available from the corresponding author upon reasonable request.
Code availability
The code used in this study is not publicly available due to intellectual property and commercial constraints, but is available from the corresponding author upon reasonable request.
References
Fan, C. et al. Current status and future directions of research on artificial intelligence in nasopharyngolaryngoscopy. Respiration 104, 255–263 (2025).
Geleijnse, G. et al. Edge enhancement optimization in flexible endoscopic images to the perception of ear, nose and throat professionals. Laryngoscope 134, 842–847 (2024).
Filauro, M. et al. Office-based procedures in laryngology. Acta Otorhinolaryngol. Ital. 41, 243–247 (2021).
Laeeq, K. et al. Learning curve for competency in flexible laryngoscopy. Laryngoscope 120, 1950–1953 (2010).
Hu, R. et al. Comparison of clinical nasal endoscopy, optical biopsy, and artificial intelligence in early diagnosis and treatment planning in laryngeal cancer: a prospective observational study. Front. Oncol. 15, 1582011 (2025).
Davaris, N. et al. Flexible transnasal endoscopy with white light or narrow band imaging for the diagnosis of laryngeal malignancy: diagnostic value, observer variability and influence of previous laryngeal surgery. Eur. Arch. Otorhinolaryngol. 276, 459–466 (2019).
Zhu, J. Q. et al. Convolutional neural network based anatomical site identification for laryngoscopy quality control: a multicenter study. Am. J. Otolaryngol. 44, 103695 (2023).
Du, Y. et al. Population-based nasopharyngeal carcinoma survival in southern China. Br. J. Cancer 134, 99–107 (2026).
Pakkanen, P. et al. Survival and larynx preservation in early glottic cancer: a randomized trial comparing laser surgery and radiation therapy. Int. J. Radiat. Oncol. Biol. Phys. 113, 96–100 (2022).
Ji, M. F. et al. Incidence and mortality of nasopharyngeal carcinoma: interim analysis of a cluster randomized controlled screening trial (PRO-NPC-001) in southern China. Ann. Oncol. 30, 1630–1637 (2019).
Li, M. M. et al. Stage migration and survival trends in laryngeal cancer. Ann. Surg. Oncol. 28, 7300–7309 (2021).
Cai, M. et al. Advances and challenges in immunotherapy for locally advanced nasopharyngeal carcinoma. Cancer Treat. Rev. 131, 102840 (2024).
Zhang, S. et al. Immunosequencing identifies signatures of T cell responses for early detection of nasopharyngeal carcinoma. Cancer Cell 43, 1423–1441.e10 (2025).
Falco, M. et al. Identification and bioinformatic characterization of a serum miRNA signature for early detection of laryngeal squamous cell carcinoma. J. Transl. Med. 22, 647 (2024).
Sadée, C. et al. Medical digital twins: enabling precision medicine and medical artificial intelligence. Lancet Digit. Health 7, 100864 (2025).
Gong, E. J. et al. Characteristics of missed simultaneous gastric lesions based on double-check analysis of the endoscopic image. Clin. Endosc. 50, 261–269 (2017).
He, X. J. et al. Artificial intelligence-assisted system for the assessment of Forrest classification of peptic ulcer bleeding: a multicenter diagnostic study. Endoscopy 56, 334–342 (2024).
Robles-Medranda, C. et al. Artificial intelligence in biliopancreatic disorders: applications in cross-sectional imaging and endoscopy. Gastroenterology 169, 471–486 (2025).
Wallace, M. B. et al. Impact of artificial intelligence on miss rate of colorectal neoplasia. Gastroenterology 163, 295–304.e5 (2022).
Kader, R. et al. A novel cloud-based artificial intelligence for real-time detection of colorectal neoplasia: a randomized controlled trial (EAGLE). npj Digit. Med. 8, 1–10 (2025).
Chen, J. et al. Medical image translation with deep learning: advances, datasets and perspectives. Med. Image Anal. 103, 103605 (2025).
Celaya, A. et al. PocketNet: a smaller neural network for medical image analysis. IEEE Trans. Med. Imaging 42, 1172–1184 (2023).
Hassanzadeh, T. et al. 2D to 3D evolutionary deep convolutional neural networks for medical image segmentation. IEEE Trans. Med. Imaging 40, 712–721 (2021).
Wei, J. et al. SAM-Swin: SAM-driven dual-swin transformers with adaptive lesion enhancement for laryngo-pharyngeal tumor detection. Med. Image Anal. 109, 103906 (2025).
Saito, T. & Rehmsmeier, M. The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets. PLoS ONE 10, e0118432 (2015).
He, K., Zhang, X., Ren, S. & Sun, J.Deep residual learning for image recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition Vol. 2016, 778–770 (IEEE, 2016).
Huang, G. Densely connected convolutional networks. In Proc. IEEE Conference on Computer Vision and Pattern Recognition Vol. 2017, 2269–2261 (IEEE, 2017).
Vaswani, A. et al. Attention is all you need. In Advances in Neural Information Processing Systems 30, 5998–6008 (Curran Associates, Inc., 2017).
Foroutan, F. et al. Computer aided detection and diagnosis of polyps in adult patients undergoing colonoscopy: a living clinical practice guideline. BMJ 388, e082656 (2025).
Soleymanjahi, S. et al. Artificial intelligence-assisted colonoscopy for polyp detection: a systematic review and meta-analysis. Ann. Intern. Med. 177, 1652–1663 (2024).
Biffi, C. et al. A novel AI device for real-time optical characterization of colorectal polyps. npj Digit. Med. 5, 84 (2022).
Halvorsen, N. et al. Benefits, burden, and harms of computer aided polyp detection with artificial intelligence in colorectal cancer screening: microsimulation modelling study. BMJ Med. 4, e001446 (2025).
Hao, R. et al. Upper airway anatomical landmark dataset for automated bronchoscopy and intubation. Sci. Data 12, 1907 (2025).
Li, C. et al. Development and validation of an endoscopic images-based deep learning model for detection with nasopharyngeal malignancies. Cancer Commun. 38, 59 (2018).
Xiong, H. et al. Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images. EBioMedicine 48, 92–99 (2019).
Li, Y. et al. Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data. J. Transl. Med. 21, 698 (2023).
Yue, Y. et al. A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images. npj Digit. Med. 7, 384 (2024).
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, 209–249 (2021).
Rizk, M. K. et al. Quality indicators common to all GI endoscopic procedures. Gastrointest. Endosc. 81, 3–16 (2015).
Xin, L. et al. Utilization and quality assessment of digestive endoscopy in China: results from 5-year consecutive nationwide surveys. Chin. Med. J. 135, 2003–2010 (2022).
Wu, L. et al. Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy. Gut 68, 2161–2169 (2019).
Li, J. et al. Colorectal mucosal exposure area assessment using artificial intelligence: a multicenter prospective observational study. Endoscopy 58, 275–283 (2026).
Dong, Z. et al. Explainable artificial intelligence incorporated with domain knowledge diagnosing early gastric neoplasms under white light endoscopy. npj Digit. Med. 6, 64 (2023).
He, R. et al. Real-time artificial intelligence-assisted detection and segmentation of nasopharyngeal carcinoma using multimodal endoscopic data: a multi-center, prospective study. EClinicalMedicine 81, 103120 (2025).
Ma, Y. et al. Development and validation of an AI foundation model for endoscopic diagnosis of esophagogastric junction adenocarcinoma. EClinicalMedicine 89, 103524 (2025).
Subspecialty Group of Laryngopharyngology, Editorial Board of Chinese Journal of Otorhinolaryngology Head and Neck Surgery Expert consensus on laryngopharyngology endoscopy. Chin. J. Otorhinolaryngol. Head Neck Surg. 56, 1137–1143 (2021).
Laryngology Group, Chinese Society of Otorhinolaryngology-Head and Neck Surgery, Chinese Medical Association & related experts. Expert consensus on artificial intelligence-assisted data acquisition and clinical application in electronic nasopharyngolaryngoscopy. J. Clin. Otorhinolaryngol. Head Neck Surg. 40, 120–130 (2026).
Tan, M. & Le, Q. EfficientNet: rethinking model scaling for convolutional neural networks. In Proc. International Conference on Machine Learning Vol. 2019, 6105–6114 (PMLR, 2019).
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In Proc. Int. Conf. Learn. Represent. (ICLR, 2015).
Loshchilov, I. & Hutter, F. SGDR: stochastic gradient descent with warm restarts. In Proc. Int. Conf. Learn. Represent. (ICLR, 2017).
Shorten, C. & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. J. Big Data 6, 60 (2019).
Deng, J. et al. ImageNet: a large-scale hierarchical image database. In Proc. IEEE Conference on Computer Vision and Pattern Recognition Vol. 2009, 248–255 (IEEE, 2009).
Acknowledgements
This work was supported by the National Natural Science Foundation of China (No. 82471165, 82473271, 82273053, 82403695, 82301296 and 82301297), the Basic and Applied Research Foundation of Guangdong Province (No. 2022B1515130009), the Guangzhou Municipal Key Research and Development Program Fund (No. 2025B03J0019), Science and Technology Commission of Shanghai Municipality(grant No.23ZR1440200), Scientific research project of Health and Family Planning Commission of Huangpu District (HLM202502), Guangci-Jinshan Innovative Technology and the 5010 Clinical Research Program of Sun Yat-sen University (No. 2017004).
Author information
Authors and Affiliations
Contributions
W.L. and M.X. conceived and designed the study. Y.L., B.Y., Y.Y.L., and C.F. contributed to the provision of study data. B.L., Q.L., and W.W. developed the artificial intelligence models. W.C., Y.S., K.S., and W.Z. collected and assembled the data. Y.L., B.Y., and Y.W. performed the data analysis and interpretation. Y.L., B.Y., Y.Y.L., and C.F. drafted the manuscript. All authors reviewed and approved the final manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
About this article
Cite this article
Li, Y., Ye, B., Li, Y. et al. Real-time AI-assisted quality control during nasopharyngolaryngoscopy: a randomized controlled trial. npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02643-0
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41746-026-02643-0
