AI Medical Compendium Journal:
The Laryngoscope

Showing 41 to 50 of 64 articles

A Comparison of an Artificial Intelligence Tool to Fundamental Frequency as an Outcome Measure in People Seeking a More Feminine Voice.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: An artificial intelligence (AI) tool was developed using audio clips of cis-male and cis-female voices based on spectral analysis to assess %probability of a voice being perceived as female (%Prob♀). This program was validated ...

Deep Learning for Classification of Pediatric Otitis Media.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: To create a new strategy for monitoring pediatric otitis media (OM), we developed a brief, reliable, and objective method for automated classification using convolutional neural networks (CNNs) with images from otoscope.

Deep Learning for Voice Gender Identification: Proof-of-concept for Gender-Affirming Voice Care.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: The need for gender-affirming voice care has been increasing in the transgender population in the last decade. Currently, objective treatment outcome measurements are lacking to assess the success of these interventions. This s...

Robot-Automated Cartilage Contouring for Complex Ear Reconstruction: A Cadaveric Study.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: Auricular reconstruction requiring manual contouring of costal cartilage is complex and time consuming, which could be facilitated by a robot in a fast and precise manner. This feasibility study evaluates the accuracy and speed...

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...

Machine Learning for Predicting Complications in Head and Neck Microvascular Free Tissue Transfer.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: Machine learning (ML) is a type of artificial intelligence wherein a computer learns patterns and associations between variables to correctly predict outcomes. The objectives of this study were to 1) use a ML platform to identi...

Otoscopic diagnosis using computer vision: An automated machine learning approach.

The Laryngoscope
OBJECTIVE: Access to otolaryngology is limited by lengthy wait lists and lack of specialists, especially in rural and remote areas. The objective of this study was to use an automated machine learning approach to build a computer vision algorithm for...