AIMC Topic: Laryngoscopy
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Diagnosis of unilateral vocal fold paralysis using auto-diagnostic deep learning model.
Scientific reports
Jul 29, 2025
Unilateral vocal fold paralysis (UVFP) is a condition characterized by impaired vocal fold mobility, typically diagnosed using laryngeal videoendoscopy. While deep learning (DL) models using static images have been explored for UVFP detection, they o...
Predicting semantic segmentation quality in laryngeal endoscopy images.
PloS one
Jul 3, 2025
Endoscopy is a major tool for assessing the physiology of inner organs. Contemporary artificial intelligence methods are used to fully automatically label medical important classes on a pixel-by-pixel level. This so-called semantic segmentation is fo...
Performance of machine learning models in predicting difficult laryngoscopy in the emergency department: a single-centre retrospective study comparing with conventional regression method.
BMC emergency medicine
Feb 21, 2025
BACKGROUND: Emergency endotracheal intubation is a critical skill for managing airway emergencies in the emergency department (ED). Accurate prediction of difficult laryngoscopy is essential for improving first-attempt success, minimizing complicatio...
AI-Powered Laryngoscopy: Exploring the Future With Google Gemini.
The Laryngoscope
Feb 20, 2025
Foundation models (FMs) are general-purpose artificial intelligence (AI) neural networks trained on massive datasets, including code, text, audio, images, and video, to handle myriad tasks from generating texts to analyzing images or composing music....
The Future of Artificial Intelligence Using Images and Clinical Assessment for Difficult Airway Management.
Anesthesia and analgesia
Jan 10, 2025
Artificial intelligence (AI) algorithms, particularly deep learning, are automatic and sophisticated methods that recognize complex patterns in imaging data providing high qualitative assessments. Several machine-learning and deep-learning models usi...
Semi-Supervised Detection Model Based on Adaptive Ensemble Learning for Medical Images.
IEEE transactions on neural networks and learning systems
Jan 7, 2025
Introducing deep learning technologies into the medical image processing field requires accuracy guarantee, especially for high-resolution images relayed through endoscopes. Moreover, works relying on supervised learning are powerless in the case of ...
Construction of prediction model of early glottic cancer based on machine learning.
Acta oto-laryngologica
Dec 30, 2024
BACKGROUND: The early diagnosis of glottic laryngeal cancer is the key to successful treatment, and machine learning (ML) combined with narrow-band imaging (NBI) laryngoscopy provides a new idea for the early diagnosis of glottic laryngeal cancer.
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.
Current Status and Future Directions of Research on Artificial Intelligence in Nasopharyngolaryngoscopy.
Respiration; international review of thoracic diseases
Dec 2, 2024
BACKGROUND: The nasopharyngolaryngoscopy (NPL) has emerged as a valuable tool for detecting early cases of head and neck cancers. However, misdiagnoses and missed diagnoses are still common phenomena. The expertise of examining physicians often serve...
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.
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