AIMC Topic: Laryngoscopy

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Machine learning based identification of relevant parameters for functional voice disorders derived from endoscopic high-speed recordings.

Scientific reports
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
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...

Automatic Recognition of Laryngoscopic Images Using a Deep-Learning Technique.

The Laryngoscope
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
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...

Low-light image enhancement of high-speed endoscopic videos using a convolutional neural network.

Medical & biological engineering & computing
Laryngeal endoscopy is one of the primary diagnostic tools for laryngeal disorders. The main techniques are videostroboscopy and lately high-speed video endoscopy. Unfortunately, due to the restricting anatomy of the larynx and technical limitations ...

A dataset of laryngeal endoscopic images with comparative study on convolution neural network-based semantic segmentation.

International journal of computer assisted radiology and surgery
PURPOSE: Automated segmentation of anatomical structures in medical image analysis is a prerequisite for autonomous diagnosis as well as various computer- and robot-aided interventions. Recent methods based on deep convolutional neural networks (CNN)...

Quantification and Analysis of Laryngeal Closure From Endoscopic Videos.

IEEE transactions on bio-medical engineering
OBJECTIVE: At present, there are no objective techniques to quantify and describe laryngeal obstruction, and the reproducibility of subjective manual quantification methods is insufficient, resulting in diagnostic inaccuracy and a poor signal-to-nois...