AIMC Topic: Laryngoscopy

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A deep learning pipeline for automated classification of vocal fold polyps in flexible laryngoscopy.

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 Sep 11, 2023
PURPOSE: To develop and validate a deep learning model for distinguishing healthy vocal folds (HVF) and vocal fold polyps (VFP) on laryngoscopy videos, while demonstrating the ability of a previously developed informative frame classifier in facilita...
Humans Neural Networks, Computer Machine Learning Retrospective Studies Vocal Cords Deep Learning Laryngoscopy Polyps
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Assessment of stress response due to C-Mac D-blade guided videolaryngoscopic endotracheal intubation and docking of da Vinci surgical robot using perfusion index in patients undergoing transoral robotic oncosurgery.

Journal of clinical monitoring and computing Apr 24, 2023
Clinical utility of perfusion index (PI) has entered a new realm as a non-invasive, quantitative index of stress response to endotracheal intubation. Transoral robotic surgery (TORS) involves F-K retractor aided docking of the surgical robot producin...
Robotic Surgical Procedures Laryngoscopy Intubation, Intratracheal Perfusion Index Adult Humans Cohort Studies Prospective Studies Robotics
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Artificial Intelligence for Upper Aerodigestive Tract Endoscopy and Laryngoscopy: A Guide for Physicians and State-of-the-Art Review.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery Apr 13, 2023
OBJECTIVE: The endoscopic and laryngoscopic examination is paramount for laryngeal, oropharyngeal, nasopharyngeal, nasal, and oral cavity benign lesions and cancer evaluation. Nevertheless, upper aerodigestive tract (UADT) endoscopy is intrinsically ...
Laryngoscopy Machine Learning Physicians Endoscopy Humans Artificial Intelligence
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Support of deep learning to classify vocal fold images in flexible laryngoscopy.

American journal of otolaryngology Feb 24, 2023
PURPOSE: To collect a dataset with adequate laryngoscopy images and identify the appearance of vocal folds and their lesions in flexible laryngoscopy images by objective deep learning models.
Deep Learning Vocal Cords Laryngoscopy Humans
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Convolutional neural network based anatomical site identification for laryngoscopy quality control: A multicenter study.

American journal of otolaryngology Nov 24, 2022
OBJECTIVES: Video laryngoscopy is an important diagnostic tool for head and neck cancers. The artificial intelligence (AI) system has been shown to monitor blind spots during esophagogastroduodenoscopy. This study aimed to test the performance of AI-...
Neural Networks, Computer Artificial Intelligence Retrospective Studies Humans Head and Neck Neoplasms Laryngoscopy
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Critical element prediction of tracheal intubation difficulty: Automatic Mallampati classification by jointly using handcrafted and attention-based deep features.

Computers in biology and medicine Oct 4, 2022
Preoperative assessment of the difficulty of tracheal intubation is of great importance in anesthesia practice because failed intubation can lead to severe complications and even death. The Mallampati score is widely used as a critical assessment cri...
Intubation, Intratracheal Artificial Intelligence Anesthesia Laryngoscopy
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Deep-Learning-Based Representation of Vocal Fold Dynamics in Adductor Spasmodic Dysphonia during Connected Speech in High-Speed Videoendoscopy.

Journal of voice : official journal of the Voice Foundation Sep 23, 2022
OBJECTIVE: Adductor spasmodic dysphonia (AdSD) is a neurogenic dystonia, which causes spasms of the laryngeal muscles. This disorder mainly affects production of connected speech. To understand how AdSD affects vocal fold (VF) movements and hence, th...
Deep Learning Predictive Value of Tests Video Recording Image Interpretation, Computer-Assisted Speech Acoustics Vocal Cords Male Middle Aged Case-Control Studies Adult Humans Biomechanical Phenomena Female Phonation Reproducibility of Results Laryngoscopy Voice Quality Vibration Dysphonia Speech Production Measurement
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Diagnosis of Early Glottic Cancer Using Laryngeal Image and Voice Based on Ensemble Learning of Convolutional Neural Network Classifiers.

Journal of voice : official journal of the Voice Foundation Sep 6, 2022
OBJECTIVES: The purpose of study is to improve the classification accuracy by comparing the results obtained by applying decision tree ensemble learning, which is one of the methods to increase the classification accuracy for a relatively small datas...
Laryngeal Neoplasms Glottis Sound Recordings Video Recording Voice Laryngoscopy Early Detection of Cancer Decision Trees Humans Retrospective Studies Adult Male Diagnosis, Computer-Assisted Convolutional Neural Networks
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A Deep Learning Approach for Quantifying Vocal Fold Dynamics During Connected Speech Using Laryngeal High-Speed Videoendoscopy.

Journal of speech, language, and hearing research : JSLHR May 23, 2022
PURPOSE: Voice disorders are best assessed by examining vocal fold dynamics in connected speech. This can be achieved using flexible laryngeal high-speed videoendoscopy (HSV), which enables us to study vocal fold mechanics with high temporal details....
Voice Disorders Laryngoscopy Vibration Video Recording Humans Vocal Cords Phonation Deep Learning Larynx Speech Glottis
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Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward Real-Time Laryngeal Cancer Detection.

The Laryngoscope Nov 25, 2021
OBJECTIVES: To assess a new application of artificial intelligence for real-time detection of laryngeal squamous cell carcinoma (LSCC) in both white light (WL) and narrow-band imaging (NBI) videolaryngoscopies based on the You-Only-Look-Once (YOLO) d...
Laryngoscopes Retrospective Studies Humans Laryngeal Neoplasms Deep Learning Narrow Band Imaging Artificial Intelligence Laryngoscopy
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