Real-time artificial intelligence-assisted detection and segmentation of nasopharyngeal carcinoma using multimodal endoscopic data: a multi-center, prospective study

Background Nasopharyngeal carcinoma (NPC) is a common malignancy in southern China, and often under- diagnosed due to reliance on physician expertise. Artificial intelligence (AI) can enhance diagnostic accuracy and efficiency using large datasets and advanced algorithms. Methods Nasal endoscopy videos with white light imaging (WLI) and narrow-band imaging (NBI) modes from 707 patients treated at one center in China from June 2020 to December 2022 were prospectively collected. A total of 8816 frames were obtained through standardized data procedures. Nasopharyngeal Carcinoma Diagnosis Segmentation Network Framework (NPC-SDNet) was developed and internally tested based on these frames. Two hundred frames were randomly selected to compare the diagnostic performance between NPC-SDNet and rhinologists. Two external testing sets with 2818 images from other hospitals validated the robustness and generalizability of the model. This study was registered at clinicaltrials.gov (NCT04547673). Findings The diagnostic accuracy, precision, recall, and specificity of NPC-SDNet using WLI were 95.0% (95% CI: 94.1%-96.2%), 93.5% (95% CI: 90.2%-95.2%), 97.2% (95% CI: 96.2%-98.3%), and 93.5% (95% CI: 91.7%- 94.0%), respectively, and using NBI were 95.8% (95% CI: 94.0%-96,8%), 93.1% (95% CI: 91.0%-95.6%), 96.0% (95% CI: 95.7%-96.8%), and 97.2% (95% CI: 97.1%-97.4%), respectively. Segmentation performance was also robust, with mean Intersection over Union scores of 83.4% (95% CI: 81.8%-85.6%; NBI) and 83.7% (95% CI: 85.1%-90.1%; WLI). In head-to-head comparisons with rhinologists, NPC-SDNet achieved a diagnostic accuracy of 94.0% (95% CI: 91.5%-95.8%) and processed 1000 frames per minute, outperforming clinicians (68.9%-88.2%) across different expertise levels. External validation further supported the reliability of NPC-SDNet, with area under the receiver operating characteristic curve (AUC) values of 0.998 and 0.977 in NBI images, 0.977 and 0.970 in WLI images. Interpretation NPC-SDNet demonstrates excellent real-time diagnostic and segmentation accuracy, offering a promising tool for enhancing the precision of NPC diagnosis. Funding This work was supported by National Key R&D Program of China (2020YFC1316903), the National Natural Science Foundation of China (NSFC) grants (81900918, 82020108009), Natural Science Foundation of Guangdong Province (2022A1515010002), Key-Area Research and Development of Guangdong Province (2023B1111040004, 2020B1111190001), and Key Clinical Technique of Guangzhou (2023P-ZD06). Copyright (c) 2025 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).