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

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 …

Objective Early recognition and referral are crucial for voice disorder management. Limited
availability of subspecialists, poor primary care awareness, and the need for specialized …

Background When investigating voice disorders a series of processes are used when
including voice screening and diagnosis. Both methods have limited standardized tests …

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

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 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 …

Pathological voice characterization has received increasing attention over the last 20 years.
Hundreds of studies have been published showing inventive approaches with very …

Introduction Over the past decade, several machine learning (ML) algorithms have been
investigated to assess their efficacy in detecting voice disorders. Literature indicates that ML …

Background Normal voice production depends on the synchronized cooperation of multiple
physiological systems, which makes the voice sensitive to changes. Any systematic …