ArticleAnnals of translational medicine2021
A deep convolutional neural network-based method for laryngeal squamous cell carcinoma diagnosis.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 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
- Morin attenuates vancomycin-induced nephrotoxicity via Nrf-2/HO-1, SIRT1/PGC1α and AKT/FOXO1A pathways.Molecular biology reports · 2026Article
- Real-Time AI-Based Assessment of Glottic Lesions During Flexible Laryngoscopy: Comparison with Expert Evaluation and Histopathology.Medical sciences (Basel, Switzerland) · 2026Article
- Decoding the complex nexus: the mechanistic interplay of hyperhomocysteinemia in brain aging and neurological disorders.Biogerontology · 2026Review
- Application of Digital Medicine to the Diagnosis and Treatment of Head and Neck Tumors.Cancer reports (Hoboken, N.J.) · 2026Review
- Artificial Intelligence Basics for Head and Neck Pathologists: A Simple Guide.Head and neck pathology · 2026Review
- Perpendicular Vascular Changes in NBI-CE of Laryngeal Lesions: Diagnostic Accuracy, Reproducibility, and Common Pitfalls.Diagnostics (Basel, Switzerland) · 2025Article
- Telemedicine follow-up for pre-malignant and malignant glottic lesions: a randomised controlled trial study protocol comparing care close to home versus standard of care.Acta oncologica (Stockholm, Sweden) · 2025Article
- Investigating the key principles in two-step heterogeneous transfer learning for early laryngeal cancer identification.Scientific reports · 2025Article
- Enhancing clinical diagnosis of laryngeal cancer through fusion-based transfer learning with Osprey Optimisation Algorithm using histology images.Frontiers in oncology · 2025Article
- Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model.Health information science and systems · 2024Article
- Sociodemographic reporting in videomics research: a review of practices in otolaryngology - head and neck surgery.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 · 2024Review
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- The impact of obesity, age, and gender on plasmatic levels of selected glycoprotein biomarkers and miRNA-499 in OSA patients.Journal of applied biomedicine · 2024Article
- Deep learning algorithm for the automated detection and classification of nasal cavity mass in nasal endoscopic images.PloS one · 2024Article
- Real-time detection of laryngopharyngeal cancer using an artificial intelligence-assisted system with multimodal data.Journal of translational medicine · 2023Article
- Modulation of semaphorin 3C & 4D expression in cancerous tissues from individuals with laryngeal squamous cell carcinoma.The Indian journal of medical research · 2023Article
- The importance of preoperative and perioperative Narrow Band Imaging endoscopy in the diagnosis of pre-tumor and tumor lesions of the larynx.Journal of applied biomedicine · 2023Article
- Hierarchical dynamic convolutional neural network for laryngeal disease classification.Scientific reports · 2022Article
- Artificial intelligence in clinical endoscopy: Insights in the field of videomics.Frontiers in surgery · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLaryngeal squamous cell carcinoma (LSCC) is one of the most common tumors of the respiratory tract. Currently, the diagnosis of LSCC is mainly based on a laryngoscopy analysis and pathological findings. Deep-learning algorithms have been shown to provide accurate clinical diagnoses.
methodsWe developed a deep convolutional neural network (CNN) model, and evaluated its application to narrow-band imaging (NBI) endoscopy and pathological diagnoses of LSCC at several hospitals. A total of 4,591 patients' laryngeal NBI scans (1,927 benign and 2,664 LSCC) were used to test and validate the model. Additionally, 3,458 pathological images (752 benign and 2,706 LSCC) of 1,228 patients' hematoxylin and eosin staining slides (318 benign and 910 LSCC) were used for the pathological diagnosis training and validation. The images were randomly divided into training, validation and testing images at the ratio of 70:15:15. An independent test cohort of LSCC NBI scans and pathological images from other institutions were also used.
resultsIn the NBI group, the areas under the curve of the validation, test, and independent test data sets were 0.966, 0.964, and 0.873, respectively, and those of the pathology group were 0.994, 0.981, and 0.982, respectively. Our method was highly accurate at diagnosing LSCC.
conclusionsIn this study, the CNN model performed well in the NBI and pathological diagnosis of LSCC. More accurate and faster diagnoses could be achieved with the assistance of this algorithm.
Indexed as
Identifiers
What OpenQuestion holds
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.