ArticleJournal of medical imaging (Bellingham, Wash.)2017
Confident texture-based laryngeal tissue classification for early stage diagnosis support.
Article in Journal of medical imaging (Bellingham, Wash.), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers, 2 of them syntheses that pooled it.
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The trial behind it
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Who cites it
27 citing papers in PubMed, 2 syntheses or guidelines pooled it, 82 citations in OpenAlex.
- A Systematic Review of the Clinical Impact of Implementing Artificial Intelligence in Upper Aerodigestive Tract Endoscopy.Head & neck · 2025Pooled it
- Application of artificial intelligence in laryngeal lesions: a systematic review and meta-analysis.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 · 2025Pooled it
- Use of a Preliminary Artificial Intelligence-Based Laryngeal Cancer Screening Framework for Low-Resource Settings: Development and Validation Study.JMIR formative research · 2025Article
- Laryngeal Cancer in the Modern Era: Evolving Trends in Diagnosis, Treatment, and Survival Outcomes.Journal of clinical medicine · 2025Review
- The Transformative Role of Artificial Intelligence in Dentistry: A Comprehensive Overview. Part 1: Fundamentals of AI, and its Contemporary Applications in Dentistry.International dental journal · 2025Review
- Enhancing Laryngeal Spinocellular Carcinoma Image Security with DCT.Indian journal of otolaryngology and head and neck surgery : official publication of the Association of Otolaryngologists of India · 2024Article
- Contact Endoscopy - Narrow Band Imaging (CE-NBI) data set for laryngeal lesion assessment.Scientific data · 2023Article
- Comparison of Different Convolutional Neural Network Activation Functions and Methods for Building Ensembles for Small to Midsize Medical Data Sets.Sensors (Basel, Switzerland) · 2022Article
- [Application and development of voice analysis and endoscopic technology combined with artificial intelligence in the diagnosis and treatment of throat disease].Lin chuang er bi yan hou tou jing wai ke za zhi = Journal of clinical otorhinolaryngology head and neck surgery · 2022Review
- Artificial Intelligence in Laryngeal Endoscopy: Systematic Review and Meta-Analysis.Journal of clinical medicine · 2022Review
- Introduction of a novel telescopic pathway to streamline 2-week-wait suspected head and neck cancer referrals and improve efficiency: A prospective service evaluation.Laryngoscope investigative otolaryngology · 2022Article
- Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging.Sensors (Basel, Switzerland) · 2021Article
- Deep Features for Training Support Vector Machines.Journal of imaging · 2021Article
- Validation of the European Laryngological Society classification of glottic vascular changes as seen by narrow band imaging in the optical biopsy setting.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 · 2021Article
- Artificial Intelligence-based methods in head and neck cancer diagnosis: an overview.British journal of cancer · 2021Review
- Cyclist Effort Features: A Novel Technique for Image Texture Characterization Applied to Larynx Cancer Classification in Contact Endoscopy-Narrow Band Imaging.Diagnostics (Basel, Switzerland) · 2021Article
- Estimation of laryngeal closure duration during swallowing without invasive X-rays.Future generations computer systems : FGCS · 2021Article
- μRALP and Beyond: Micro-Technologies and Systems for Robot-Assisted Endoscopic Laser Microsurgery.Frontiers in robotics and AI · 2021Review
- Heartbeat Detection by Laser Doppler Vibrometry and Machine Learning.Sensors (Basel, Switzerland) · 2020Article
- Explainable Deep Learning Models in Medical Image Analysis.Journal of imaging · 2020Review
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early stage diagnosis of laryngeal squamous cell carcinoma (SCC) is of primary importance for lowering patient mortality or after treatment morbidity. Despite the challenges in diagnosis reported in the clinical literature, few efforts have been invested in computer-assisted diagnosis. The objective of this paper is to investigate the use of texture-based machine-learning algorithms for early stage cancerous laryngeal tissue classification. To estimate the classification reliability, a measure of confidence is also exploited. From the endoscopic videos of 33 patients affected by SCC, a well-balanced dataset of 1320 patches, relative to four laryngeal tissue classes, was extracted. With the best performing feature, the achieved median classification recall was 93% [interquartile range [Formula: see text]]. When excluding low-confidence patches, the achieved median recall was increased to 98% ([Formula: see text]), proving the high reliability of the proposed approach. This research represents an important advancement in the state-of-the-art computer-assisted laryngeal diagnosis, and the results are a promising step toward a helpful endoscope-integrated processing system to support early stage diagnosis.
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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.