Objective This study investigated whether artificial intelligence (AI) models combining voice
signals, demographics, and structured medical records can detect glottic neoplasm from …

Objectives This study aimed to evaluate the performance of artificial intelligence (AI) models
using connected speech and vowel sounds in detecting benign laryngeal diseases. Study …

Objective Survey the current literature on artificial intelligence (AI) applications for detecting
and classifying vocal pathology using voice recordings, and identify challenges and …

Use of artificial intelligence in medical practice has considerably increased in recent years.
Machine learning algorithms have been successfully applied to detect abnormal voice …

Objective Defining a clinician's ability to perceptually identify mass from voice will inform the
feasibility, design priorities, and performance standards for tools developed to screen for …

Voice change is often the first sign of laryngeal cancer, leading to diagnosis through hospital
laryngoscopy. Screening for laryngeal cancer solely based on voice could enhance early …

Objectives The purpose of study is to improve the classification accuracy by comparing the
results obtained by applying decision tree ensemble learning, which is one of the methods …

Objective To summarize the use of deep learning in the detection of voice disorders using
acoustic and laryngoscopic input, compare specific neural networks in terms of accuracy …

Objectives Diagnostic tools for voice disorders are lacking for primary care physicians.
Artificial intelligence (AI) tools may add to the armamentarium for physicians, decreasing the …

Voice changes may be the earliest signs in laryngeal cancer. We investigated whether
automated voice signal analysis can be used to distinguish patients with laryngeal cancer …