AI Medical Compendium Topic

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Laryngoscopy

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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...

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...

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 ...

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...

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)...

Learning-based classification of informative laryngoscopic frames.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Early-stage diagnosis of laryngeal cancer is of primary importance to reduce patient morbidity. Narrow-band imaging (NBI) endoscopy is commonly used for screening purposes, reducing the risks linked to a biopsy but at the co...

A Novel Artificial Intelligence System for Endotracheal Intubation.

Prehospital emergency care
OBJECTIVE: Adequate visualization of the glottic opening is a key factor to successful endotracheal intubation (ETI); however, few objective tools exist to help guide providers' ETI attempts toward the glottic opening in real-time. Machine learning/a...