AIMC Topic: Laryngoscopy

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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 ...
Physicians Endoscopy Laryngoscopy Humans Artificial Intelligence Machine Learning
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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-...
Head and Neck Neoplasms Laryngoscopy Humans Neural Networks, Computer Artificial Intelligence Retrospective Studies
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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...
Laryngoscopy Intubation, Intratracheal Artificial Intelligence Anesthesia
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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...
Voice Quality Dysphonia Speech Acoustics Vocal Cords Laryngoscopy Predictive Value of Tests Vibration Female Video Recording Speech Production Measurement Reproducibility of Results Image Interpretation, Computer-Assisted Phonation Adult Case-Control Studies Biomechanical Phenomena Deep Learning Male Humans Middle Aged
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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...
Retrospective Studies Adult Male Humans Decision Trees Video Recording Voice Diagnosis, Computer-Assisted Convolutional Neural Networks Early Detection of Cancer Sound Recordings Laryngoscopy Laryngeal Neoplasms Glottis
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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....
Humans Deep Learning Speech Video Recording Vocal Cords Voice Disorders Larynx Glottis Laryngoscopy Vibration Phonation
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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 Laryngeal Neoplasms Narrow Band Imaging Retrospective Studies Humans Deep Learning Artificial Intelligence Laryngoscopy
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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...
Predictive Value of Tests Feasibility Studies Image Interpretation, Computer-Assisted Retrospective Studies Deep Learning Larynx Humans Datasets as Topic ROC Curve Laryngoscopy Laryngeal Diseases
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A Deep Learning Enhanced Novel Software Tool for Laryngeal Dynamics Analysis.

Journal of speech, language, and hearing research : JSLHR May 17, 2021
Purpose High-speed videoendoscopy (HSV) is an emerging, but barely used, endoscopy technique in the clinic to assess and diagnose voice disorders because of the lack of dedicated software to analyze the data. HSV allows to quantify the vocal fold osc...
Laryngoscopy Deep Learning Humans Larynx Vibration Software Video Recording Glottis Phonation Vocal Cords
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