Deep Learning Application for Vocal Fold Disease Prediction Through Voice Recognition: Preliminary Development Study.

ABSTRACT:

Background

Dysphonia influences the quality of life by interfering with communication. However, a laryngoscopic examination is expensive and not readily accessible in primary care units. Experienced laryngologists are required to achieve an accurate diagnosis.

Objective

This study sought to detect various vocal fold diseases through pathological voice recognition using artificial intelligence.

Methods

We collected 189 normal voice samples and 552 samples of individuals with voice disorders, including vocal atrophy (n=224), unilateral vocal paralysis (n=50), organic vocal fold lesions (n=248), and adductor spasmodic dysphonia (n=30). The 741 samples were divided into 2 sets: 593 samples as the training set and 148 samples as the testing set. A convolutional neural netw

SUBMITTER: Hu HC 

PROVIDER: S-EPMC8241431 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature