Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data

Sep 24, 2026

Uploaded by McQueen

Laryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal disease where early diagnosis improves outcomes, yet biopsy guidance during laryngoscopy relies heavily on specialist expertise, increasing missed cases and repeated unnecessary procedures. A deep convolutional neural networkbased Laryngopharyngeal Artificial Intelligence Diagnostic System (LPAIDS) was developed to automatically identify LPC in real time using laryngoscopy white-light imaging (WLI) and narrow-band imaging (NBI). Training used 31,543 images from 2382 patients and external validation used images from five hospitals.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Background

Methods

Results

Conclusions

Li et al. Journal of Translational Medicine (2023) 21:698 [https://doi.org/10.1186/s12967-023-04572-y](https://doi.org/10.1186/s12967-023-04572-y) Journal of Translational Medicine RESEARCH Open Access Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data Yun Li1†, Wenxin Gu2†, Huijun Yue1†, Guoqing Lei1, Wenbin Guo1, Yihui Wen1, Haocheng Tang3, Xin Luo4, Wenjuan Tu4, Jin Ye4, Ruomei Hong5, Qian Cai5, Qingyu Gu6, Tianrun Liu6, Beiping Miao7, Ruxin Wang8, Jiangtao Ren2* and Wenbin Lei 1* Abstract Background Laryngopharyngeal cancer (LPC) includes laryngeal and hypopharyngeal cancer, whose early diagnosis can significantly improve the prognosis and quality of life of patients. Pathological biopsy of suspicious cancerous tissue under the guidance of laryngoscopy is the gold standard for diagnosing LPC. However, this subjective examination largely depends on the skills and experience of laryngologists, which increases the possibility of missed diagnoses and repeated unnecessary biopsies. We aimed to develop and validate a deep convolutional neural networkbased Laryngopharyngeal Artificial Intelligence Diagnostic System (LPAIDS) for real-time automatically identifying LPCin both laryngoscopy white-light imaging (WLI) and narrow-band imaging (NBI) images to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists. Methods All 31,543 laryngoscopic images from 2382 patients were categorised into training, verification, and test sets to develop, validate, and internal test LPAIDS. Another 25,063 images from five other hospitals were used as external tests. Overall, 551 videos were used to evaluate the real-time performance of the system, and 200 randomly selected videos were used to compare the diagnostic performance ofthe LPAIDS with that of laryngologists. Two deep-learning models using either WLI (model W) or NBI (model N) images were constructed to compare with LPAIDS. Results LPAIDS had a higher diagnostic performance than models W and N, with accuracies of 0·956 and 0·949 in the internal image and video tests, respectively. The robustness and stability of LPAIDS were validated in external sets with the area under the receiver operating characteristic curve values of 0·965–0·987 . In the laryngologistmachine competition, LPAIDS achieved an accuracy of 0·940, which was comparable to expert laryngologists and outperformed other laryngologists with varying qualifications. Conclusions LPAIDS provided high accuracy and stability in detecting LPC in real-time, which showed great potential for using LPAIDS to improve the diagnostic accuracy of LPC by reducing diagnostic variation among on-expert laryngologists. †Yun Li, Wenxin Gu, and Huijun Yue contributed equally to this work. *Correspondence: Jiangtao Ren [issrjt@mail.sysu.edu.cn](issrjt@mail.sysu.edu.cn)[ ](issrjt@mail.sysu.edu.cn)Wenbin Lei [leiwb@mail.sysu.edu.cn](leiwb@mail.sysu.edu.cn) Full list of author information is available at the end of the article © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/](http://creativecommons.org/li

FAQs

What problem does LPAIDS aim to solve in LPC diagnosis?

LPAIDS targets diagnostic variability and missed detections that can occur because conventional LPC identification during laryngoscopy depends on laryngologists’ subjective skills and experience.

How was the LPAIDS system evaluated?

It was developed using training/verification/test sets from 31,543 laryngoscopic images and further tested externally using 25,063 images from five other hospitals. Real-time performance was assessed with 551 videos, with 200 videos used for comparisons.

Does LPAIDS work on both WLI and NBI laryngoscopy images?

Yes. The system integrates multimodal data by identifying LPC using both laryngoscopy white-light imaging (WLI) and narrow-band imaging (NBI), and performance is compared against models trained on each modality.

Sep 24, 2026

Uploaded by McQueen