ArticleNPJ digital medicine2024
A deep learning based smartphone application for early detection of nasopharyngeal carcinoma using endoscopic images.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A Systematic Review of the Clinical Impact of Implementing Artificial Intelligence in Upper Aerodigestive Tract Endoscopy.Head & neck · 2025Pooled it
- Artificial intelligence-assisted real-time nasopharyngeal cancer diagnostic model enhances rhinologist performance: a prospective multi-reader study.Annals of medicine · 2026Article
- A real-time metric for quantifying registration stability in mixed reality neurosurgical navigation.International journal of computer assisted radiology and surgery · 2026Article
- Real-time AI-assisted quality control during nasopharyngolaryngoscopy: a randomized controlled trial.NPJ digital medicine · 2026Article
- Deep Learning in Otolaryngology: A Narrative Review.JAMA otolaryngology-- head & neck surgery · 2026Review
- Use of a Preliminary Artificial Intelligence-Based Laryngeal Cancer Screening Framework for Low-Resource Settings: Development and Validation Study.JMIR formative research · 2025Article
- Nasopharyngeal Carcinoma in Southeast Asia: Current Landscape and Future Priorities.British journal of biomedical science · 2025Review
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nasal endoscopy is crucial for the early detection of nasopharyngeal carcinoma (NPC), but its accuracy relies heavily on the clinician's expertise, posing challenges for primary healthcare providers. Here, we retrospectively analysed 39,340 nasal endoscopic white-light images from three high-incidence NPC centres, utilising eight advanced deep learning models to develop an Internet-enabled smartphone application, "Nose-Keeper", that can be used for early detection of NPC and five prevalent nasal diseases and assessment of healthy individuals. Our app demonstrated a remarkable overall accuracy of 92.27% (95% Confidence Interval (CI): 90.66%-93.61%). Notably, its sensitivity and specificity in NPC detection achieved 96.39% and 99.91%, respectively, outperforming nine experienced otolaryngologists. Explainable artificial intelligence was employed to highlight key lesion areas, improving Nose-Keeper's decision-making accuracy and safety. Nose-Keeper can assist primary healthcare providers in diagnosing NPC and common nasal diseases efficiently, offering a valuable resource for people in high-incidence NPC regions to manage nasal cavity health effectively.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.