AIMC Topic: Laryngoscopy
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Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height.
BMC anesthesiology
Apr 21, 2021
BACKGROUND: Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyroment...
Application of a Computer Vision Tool for Automated Glottic Tracking to Vocal Fold Paralysis Patients.
Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery
Feb 16, 2021
OBJECTIVES: (1) Demonstrate true vocal fold (TVF) tracking software (AGATI [Automated Glottic Action Tracking by artificial Intelligence]) as a quantitative assessment of unilateral vocal fold paralysis (UVFP) in a large patient cohort. (2) Correlate...
Comparison of Convolutional Neural Network Models for Determination of Vocal Fold Normality in Laryngoscopic Images.
Journal of voice : official journal of the Voice Foundation
Aug 30, 2020
OBJECTIVES: Deep learning using convolutional neural networks (CNNs) is widely used in medical imaging research. This study was performed to investigate if vocal fold normality in laryngoscopic images can be determined by CNN-based deep learning and ...
Machine Learning in Laryngoscopy Analysis: A Proof of Concept Observational Study for the Identification of Post-Extubation Ulcerations and Granulomas.
The Annals of otology, rhinology, and laryngology
Aug 14, 2020
OBJECTIVE: Computer-aided analysis of laryngoscopy images has potential to add objectivity to subjective evaluations. Automated classification of biomedical images is extremely challenging due to the precision required and the limited amount of annot...
Machine learning based identification of relevant parameters for functional voice disorders derived from endoscopic high-speed recordings.
Scientific reports
Jun 29, 2020
In voice research and clinical assessment, many objective parameters are in use. However, there is no commonly used set of parameters that reflect certain voice disorders, such as functional dysphonia (FD); i.e. disorders with no visible anatomical c...
An Open-Source Computer Vision Tool for Automated Vocal Fold Tracking From Videoendoscopy.
The Laryngoscope
May 1, 2020
OBJECTIVES: Contemporary clinical assessment of vocal fold adduction and abduction is qualitative and subjective. Herein is described a novel computer vision tool for automated quantitative tracking of vocal fold motion from videolaryngoscopy. The po...
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.
Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique.
The Laryngoscope
Feb 18, 2020
OBJECTIVES/HYPOTHESIS: To develop a deep-learning-based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngosco...
A Convolutional Neural Network for Real Time Classification, Identification, and Labelling of Vocal Cord and Tracheal Using Laryngoscopy and Bronchoscopy Video.
Journal of medical systems
Jan 2, 2020
BACKGROUND: The use of artificial intelligence, including machine learning, is increasing in medicine. Use of machine learning is rising in the prediction of patient outcomes. Machine learning may also be able to enhance and augment anesthesia clinic...
Computer-aided diagnosis of laryngeal cancer via deep learning based on laryngoscopic images.
EBioMedicine
Oct 5, 2019
OBJECTIVE: To develop a deep convolutional neural network (DCNN) that can automatically detect laryngeal cancer (LCA) in laryngoscopic images.
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