AIMC Topic: Laryngeal Diseases
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A non-local dual-stream fusion network for laryngoscope recognition.
American journal of otolaryngology
Dec 17, 2024
PURPOSE: To use deep learning technology to design and implement a model that can automatically classify laryngoscope images and assist doctors in diagnosing laryngeal diseases.
Harnessing machine learning in diagnosing complex hoarseness cases.
American journal of otolaryngology
Dec 4, 2024
PURPOSE: Traditional vocal fold pathology recognition typically requires expertise of laryngologists and advanced instruments, primarily through direct visualization. This study aims to augment this conventional paradigm by introducing a parallel dia...
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
Nov 22, 2024
OBJECTIVE: The objective of this systematic review and meta-analysis was to evaluate the diagnostic accuracy of AI-assisted technologies, including endoscopy, voice analysis, and histopathology, for detecting and classifying laryngeal lesions.
New developments in the application of artificial intelligence to laryngology.
Current opinion in otolaryngology & head and neck surgery
Jul 24, 2024
PURPOSE OF REVIEW: The purpose of this review is to summarize the existing literature on artificial intelligence technology utilization in laryngology, highlighting recent advances and current barriers to implementation.
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
May 2, 2024
PURPOSE: Informative image selection in laryngoscopy has the potential for improving automatic data extraction alone, for selective data storage and a faster review process, or in combination with other artificial intelligence (AI) detection or diagn...
Classification of laryngeal diseases including laryngeal cancer, benign mucosal disease, and vocal cord paralysis by artificial intelligence using voice analysis.
Scientific reports
Apr 23, 2024
Voice change is often the first sign of laryngeal cancer, leading to diagnosis through hospital laryngoscopy. Screening for laryngeal cancer solely based on voice could enhance early detection. However, identifying voice indicators specific to laryng...
A multi-stage transfer learning strategy for diagnosing a class of rare laryngeal movement disorders.
Computers in biology and medicine
Sep 29, 2023
BACKGROUND: It remains hard to directly apply deep learning-based methods to assist diagnosing essential tremor of voice (ETV) and abductor and adductor spasmodic dysphonia (ABSD and ADSD). One of the main challenges is that, as a class of rare laryn...
Hierarchical dynamic convolutional neural network for laryngeal disease classification.
Scientific reports
Aug 17, 2022
Laryngeal disease classification is a relatively hard task in medical image processing resulting from its complex structures and varying viewpoints in data collection. Some existing methods try to tackle this task via the convolutional neural network...
Diagnostic Accuracies of Laryngeal Diseases Using a Convolutional Neural Network-Based Image Classification System.
The Laryngoscope
May 17, 2021
OBJECTIVES/HYPOTHESIS: There may be an interobserver variation in the diagnosis of laryngeal disease based on laryngoscopic images according to clinical experience. Therefore, this study is aimed to perform computer-assisted diagnosis for common lary...
Optical Biopsy: Automated Classification of Airway Endoscopic Findings Using a Convolutional Neural Network.
The Laryngoscope
Apr 28, 2020
OBJECTIVES/HYPOTHESIS: Create an autonomous computational system to classify endoscopy findings.
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