AI Medical Compendium Journal:
American journal of otolaryngology

Showing 1 to 10 of 21 articles

A non-local dual-stream fusion network for laryngoscope recognition.

American journal of otolaryngology
PURPOSE: To use deep learning technology to design and implement a model that can automatically classify laryngoscope images and assist doctors in diagnosing laryngeal diseases.

Harnessing machine learning in diagnosing complex hoarseness cases.

American journal of otolaryngology
PURPOSE: Traditional vocal fold pathology recognition typically requires expertise of laryngologists and advanced instruments, primarily through direct visualization. This study aims to augment this conventional paradigm by introducing a parallel dia...

Predicting sinonasal inverted papilloma attachment using machine learning: Current lessons and future directions.

American journal of otolaryngology
BACKGROUND: Hyperostosis is a common radiographic feature of inverted papilloma (IP) tumor origin on computed tomography (CT). Herein, we developed a machine learning (ML) model capable of analyzing CT images and identifying IP attachment sites.

How reliable is artificial intelligence in the diagnosis of cholesteatoma on CT images?

American journal of otolaryngology
PURPOSE: This study analysed the main artificial intelligence (AI) models for the diagnosis of cholesteatoma on computed tomography (CT), evaluating their performance and comparing them with each other. The increasing application of AI in radiology r...

A lightweight intelligent laryngeal cancer detection system for rural areas.

American journal of otolaryngology
OBJECTIVE: Early diagnosis of laryngeal cancer (LC) is crucial, particularly in rural areas. Despite existing studies on deep learning models for LC identification, challenges remain in selecting suitable models for rural areas with shortages of lary...

Multi-instance learning based artificial intelligence model to assist vocal fold leukoplakia diagnosis: A multicentre diagnostic study.

American journal of otolaryngology
OBJECTIVE: To develop a multi-instance learning (MIL) based artificial intelligence (AI)-assisted diagnosis models by using laryngoscopic images to differentiate benign and malignant vocal fold leukoplakia (VFL).

Fully automated 3D machine learning model for HPV status characterization in oropharyngeal squamous cell carcinomas based on CT images.

American journal of otolaryngology
BACKGROUND: Human papillomavirus (HPV) status plays a major role in predicting oropharyngeal squamous cell carcinoma (OPSCC) survival. This study assesses the accuracy of a fully automated 3D convolutional neural network (CNN) in predicting HPV statu...

Pediatric tympanostomy tube assessment via deep learning.

American journal of otolaryngology
PURPOSE: Tympanostomy tube (TT) placement is the most frequently performed ambulatory surgery in children under 15. After the procedure it is recommended that patients follow up regularly for "tube checks" until TT extrusion. Such visits incur direct...