ArticlePloS one2024
Deep learning algorithm for the automated detection and classification of nasal cavity mass in nasal endoscopic images.
Article in PloS one, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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10 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- A Systematic Review of the Clinical Impact of Implementing Artificial Intelligence in Upper Aerodigestive Tract Endoscopy.Head & neck · 2025Pooled it
- Artificial Intelligence in Rhinology: A State-of-the-Art Review of Clinical Readiness and Implementation Pathways.Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery · 2026Review
- Deep Learning for Endoscopic Classification of Adenoid Hypertrophy.World journal of otorhinolaryngology - head and neck surgery · 2026Article
- Can a Rule-Based Expert System Diagnose Nasal Obstruction from Nasoendoscopy Videos?Facial plastic surgery : FPS · 2026Article
- Machine Learning-Enhanced Clinical Decision Support for Diagnosing Sinusitis With Nasal Endoscopy.International forum of allergy & rhinology · 2026Article
- Deep learning using nasal endoscopy and T2-weighted MRI for prediction of sinonasal inverted papilloma-associated squamous cell carcinoma: an exploratory study.European radiology experimental · 2025Article
- Artificial Intelligence Assurance in Head and Neck Surgery: Now and Next.Proceedings. IEEE International Symposium on Computer-Based Medical Systems · 2025Article
- Machine Learning of Endoscopy Images to Identify, Classify, and Segment Sinonasal Masses.International forum of allergy & rhinology · 2025Article
- Comparison of clinical nasal endoscopy, optical biopsy, and artificial intelligence in early diagnosis and treatment planning in laryngeal cancer: a prospective observational study.Frontiers in oncology · 2025Article
- Artificial intelligence for automatic detection and segmentation of nasal polyposis: a pilot study.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2024Article
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Authors and funding
21 authors at 14 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Nasal endoscopy is routinely performed to distinguish the pathological types of masses. There is a lack of studies on deep learning algorithms for discriminating a wide range of endoscopic nasal cavity mass lesions. Therefore, we aimed to develop an endoscopic-examination-based deep learning model to detect and classify nasal cavity mass lesions, including nasal polyps (NPs), benign tumors, and malignant tumors. The clinical feasibility of the model was evaluated by comparing the results to those of manual assessment. Biopsy-confirmed nasal endoscopic images were obtained from 17 hospitals in South Korea. Here, 400 images were used for the test set. The training and validation datasets consisted of 149,043 normal nasal cavity, 311,043 NP, 9,271 benign tumor, and 5,323 malignant tumor lesion images. The proposed Xception architecture achieved an overall accuracy of 0.792 with the following class accuracies on the test set: normal = 0.978 ± 0.016, NP = 0.790 ± 0.016, benign = 0.708 ± 0.100, and malignant = 0.698 ± 0.116. With an average area under the receiver operating characteristic curve (AUC) of 0.947, the AUC values and F1 score were highest in the order of normal, NP, malignant tumor, and benign tumor classes. The classification performances of the proposed model were comparable with those of manual assessment in the normal and NP classes. The proposed model outperformed manual assessment in the benign and malignant tumor classes (sensitivities of 0.708 ± 0.100 vs. 0.549 ± 0.172, 0.698 ± 0.116 vs. 0.518 ± 0.153, respectively). In urgent (malignant) versus nonurgent binary predictions, the deep learning model achieved superior diagnostic accuracy. The developed model based on endoscopic images achieved satisfactory performance in classifying four classes of nasal cavity mass lesions, namely normal, NP, benign tumor, and malignant tumor. The developed model can therefore be used to screen nasal cavity lesions accurately and rapidly.
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