ArticleThe Laryngoscope2022
Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward Real-Time Laryngeal Cancer Detection.
Article in The Laryngoscope, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07596355 (Head&Neck Application of Novel Computer-assisted Endoscopy), which is not on this map. Cited by 40 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Head&Neck Application of Novel Computer-assisted Endoscopy
Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 111 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 Laryngeal Cancer Detection: A Systematic Review and Meta-Analysis.Current oncology (Toronto, Ont.) · 2025Pooled it
- Deep Learning for Automatic Segmentation of Pituitary Adenomas: A Videomics Study.Operative neurosurgery (Hagerstown, Md.) · 2026Article
- Contemporary Indications and Technical Limits of Transoral Laser Microsurgery for Laryngeal Squamous Cell Carcinoma.Advances in therapy · 2026Review
- Artificial Intelligence in Head and Neck Surgical Oncology: A State-of-the-Art Review.Journal of clinical medicine · 2026Review
- [Expert consensus on artificial intelligence-assisted data acquisition and clinical application in electronic nasopharyngolaryngoscopy].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2026Article
- Advancements in Image-Based Artificial Intelligence in the Diagnosis and Treatment of Head and Neck Squamous Cell Carcinoma: A Narrative Review.International journal of general medicine · 2026Review
- A Panoramic View of Narrow Band Imaging in the Treatment of Head and Neck CancerCurrent medical imaging · 2026Review
- A transformer-based multi-modal fusion framework for laryngeal lesion classification using contact endoscopy.Frontiers in surgery · 2026Article
- A deep learning fusion network trained with medical records and laryngoscopic images in the early diagnosis of glottic carcinoma.iScience · 2025Article
- Perpendicular Vascular Changes in NBI-CE of Laryngeal Lesions: Diagnostic Accuracy, Reproducibility, and Common Pitfalls.Diagnostics (Basel, Switzerland) · 2025Article
- Democratizing cancer detection: artificial intelligence-enhanced endoscopy could address global disparities in head and neck cancer outcomes.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 · 2025Review
- Recent advance in early oral lesion diagnosis: the application of artificial intelligence-assisted endoscopy.Frontiers in oncology · 2025Review
- Artificial intelligence based diagnosis of sulcus: assesment of videostroboscopy via deep learning.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
- Enhanced WGAN Model for Diagnosing Laryngeal Carcinoma.Cancers · 2024Article
- An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning.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
- [The effect of narrow band imaging in CO2 laser therapy in early-stage glottic cancer].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2024Article
- Diagnosis of cervical lymphoma using a YOLO-v7-based model with transfer learning.Scientific reports · 2024Article
- Exploring the landscape of AI-assisted decision-making in head and neck cancer treatment: a comparative analysis of NCCN guidelines and ChatGPT responses.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
- A colonial serrated polyp classification model using white-light ordinary endoscopy images with an artificial intelligence model and TensorFlow chart.BMC gastroenterology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo assess a new application of artificial intelligence for real-time detection of laryngeal squamous cell carcinoma (LSCC) in both white light (WL) and narrow-band imaging (NBI) videolaryngoscopies based on the You-Only-Look-Once (YOLO) deep learning convolutional neural network (CNN). STUDY
designExperimental study with retrospective data.
methodsRecorded videos of LSCC were retrospectively collected from in-office transnasal videoendoscopies and intraoperative rigid endoscopies. LSCC videoframes were extracted for training, validation, and testing of various YOLO models. Different techniques were used to enhance the image analysis: contrast limited adaptive histogram equalization, data augmentation techniques, and test time augmentation (TTA). The best-performing model was used to assess the automatic detection of LSCC in six videolaryngoscopies.
resultsTwo hundred and nineteen patients were retrospectively enrolled. A total of 624 LSCC videoframes were extracted. The YOLO models were trained after random distribution of images into a training set (82.6%), validation set (8.2%), and testing set (9.2%). Among the various models, the ensemble algorithm (YOLOv5s with YOLOv5m-TTA) achieved the best LSCC detection results, with performance metrics in par with the results reported by other state-of-the-art detection models: 0.66 Precision (positive predicted value), 0.62 Recall (sensitivity), and 0.63 mean Average Precision at 0.5 intersection over union. Tests on the six videolaryngoscopies demonstrated an average computation time per videoframe of 0.026 seconds. Three demonstration videos are provided.
conclusionThis study identified a suitable CNN model for LSCC detection in WL and NBI videolaryngoscopies. Detection performances are highly promising. The limited complexity and quick computational times for LSCC detection make this model ideal for real-time processing. LEVEL OF EVIDENCE: 3 Laryngoscope, 132:1798-1806, 2022.
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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.