ArticleScientific reports2022
Deep learning model for tongue cancer diagnosis using endoscopic images.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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17 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
- Application of Digital Medicine to the Diagnosis and Treatment of Head and Neck Tumors.Cancer reports (Hoboken, N.J.) · 2026Review
- From remote screening to precision prevention: responsible multimodal AI for risk prediction and equitable oral healthcare.Frontiers in oral health · 2026Review
- Artificial Intelligence in the Diagnosis of Tongue Cancer: A Systematic Review with Meta-Analysis.Biomedicines · 2025Review
- 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
- Machine Learning of Endoscopy Images to Identify, Classify, and Segment Sinonasal Masses.International forum of allergy & rhinology · 2025Article
- Recent advance in early oral lesion diagnosis: the application of artificial intelligence-assisted endoscopy.Frontiers in oncology · 2025Review
- SCC-NET: segmentation of clinical cancer image for head and neck squamous cell carcinoma.Journal of medical imaging (Bellingham, Wash.) · 2024Article
- Artificial intelligence in tongue diagnosis: classification of tongue lesions and normal tongue images using deep convolutional neural network.BMC medical imaging · 2024Article
- Enhancing deep learning classification performance of tongue lesions in imbalanced data: mosaic-based soft labeling with curriculum learning.BMC oral health · 2024Article
- Deep learning algorithm for the automated detection and classification of nasal cavity mass in nasal endoscopic images.PloS one · 2024Article
- Deep Learning Techniques and Imaging in Otorhinolaryngology-A State-of-the-Art Review.Journal of clinical medicine · 2023Review
- Detection of Abnormal Changes on the Dorsal Tongue Surface Using Deep Learning.Medicina (Kaunas, Lithuania) · 2023Article
- Eugenol Induces Apoptosis in Tongue Squamous Carcinoma Cells by Mediating the Expression of Bcl-2 Family.Life (Basel, Switzerland) · 2022Article
- A study on the diagnosis of theFrontiers in microbiology · 2022Article
- EfficientNetSwift: A Lightweight and Precise Deep Learning Model for Detecting Oral Squamous Cell Carcinoma Using Pathological Images.Technology in cancer research & treatmentArticle
- Review
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we developed a deep learning model to identify patients with tongue cancer based on a validated dataset comprising oral endoscopic images. We retrospectively constructed a dataset of 12,400 verified endoscopic images from five university hospitals in South Korea, collected between 2010 and 2020 with the participation of otolaryngologists. To calculate the probability of malignancy using various convolutional neural network (CNN) architectures, several deep learning models were developed. Of the 12,400 total images, 5576 images related to the tongue were extracted. The CNN models showed a mean area under the receiver operating characteristic curve (AUROC) of 0.845 and a mean area under the precision-recall curve (AUPRC) of 0.892. The results indicate that the best model was DenseNet169 (AUROC 0.895 and AUPRC 0.918). The deep learning model, general physicians, and oncology specialists had sensitivities of 81.1%, 77.3%, and 91.7%; specificities of 86.8%, 75.0%, and 90.9%; and accuracies of 84.7%, 75.9%, and 91.2%, respectively. Meanwhile, fair agreement between the oncologist and the developed model was shown for cancer diagnosis (kappa value = 0.685). The deep learning model developed based on the verified endoscopic image dataset showed acceptable performance in tongue cancer diagnosis.
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