AIMC Topic: Laryngoscopy

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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...
Logistic Models Emergency Service, Hospital Laryngoscopy Middle Aged ROC Curve Intubation, Intratracheal Retrospective Studies Female Adult Male Aged Humans Machine Learning
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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....
Humans Neural Networks, Computer Video Recording Artificial Intelligence Laryngoscopy
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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...
Airway Management Laryngoscopy Intubation, Intratracheal Deep Learning Predictive Value of Tests Humans Machine Learning Forecasting Artificial Intelligence
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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 ...
Colonoscopy Deep Learning Image Processing, Computer-Assisted Neural Networks, Computer Algorithms Humans Laryngoscopy Supervised Machine Learning Ensemble Learning
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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.
Retrospective Studies Female Adult Male Aged Humans Machine Learning Narrow Band Imaging Laryngoscopy Middle Aged Laryngeal Neoplasms Early Detection of Cancer Glottis
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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.
Laryngoscopes Laryngeal Diseases Humans Neural Networks, Computer Image Processing, Computer-Assisted Deep Learning Laryngoscopy
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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...
Laryngoscopy Humans Artificial Intelligence Head and Neck Neoplasms
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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.
Laryngeal Diseases Sensitivity and Specificity Humans Laryngoscopy Artificial Intelligence Laryngeal Neoplasms
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Comparative Analysis of Machine-Learning Model Performance in Image Analysis: The Impact of Dataset Diversity and Size.

Anesthesia and analgesia Nov 15, 2024
BACKGROUND: This study presents an analysis of machine-learning model performance in image analysis, with a specific focus on videolaryngoscopy procedures. The research aimed to explore how dataset diversity and size affect the performance of machine...
Laryngoscopy Machine Learning Video Recording Humans Image Processing, Computer-Assisted Datasets as Topic Social Media
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Glottic opening detection using deep learning for neonatal intubation with video laryngoscopy.

Journal of perinatology : official journal of the California Perinatal Association Nov 13, 2024
OBJECTIVE: This study aimed to develop an artificial intelligence (AI) method to augment video laryngoscopy (VL) by automating the detection of the glottic opening in neonates, as a step toward future studies on improving intubation outcomes.
Laryngoscopy Intubation, Intratracheal Glottis Deep Learning Video Recording Humans Video-Assisted Techniques and Procedures Infant, Newborn Male Female
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