Deep Learning Application for Vocal Fold Disease Prediction Through Voice Recognition: Preliminary Development Study
| Autor: | Hao Chun Hu, Tzu Pei Tsai, Chuen Heng Wang, Hsiao Yun Cho, Chang Jung Lu, Yi Ting Chen, Shyue Yih Chang, Kai Jun Li, Oscar Kuang Sheng Lee |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
pathological voice
Speech recognition Computer applications to medicine. Medical informatics R858-859.7 convolutional neural network Health Informatics Physical examination Disease Vocal Cords Convolutional neural network 03 medical and health sciences 0302 clinical medicine dysphonia Deep Learning Artificial Intelligence Throat medicine Paralysis otorhinolaryngologic diseases Humans Active listening 030223 otorhinolaryngology Nose Original Paper medicine.diagnostic_test business.industry Deep learning medicine.anatomical_structure Voice Recognition 030220 oncology & carcinogenesis voice pathology identification Quality of Life Artificial intelligence medicine.symptom Public aspects of medicine RA1-1270 business vocal fold disease |
| Zdroj: | Journal of Medical Internet Research, Vol 23, Iss 6, p e25247 (2021) Journal of Medical Internet Research |
| ISSN: | 1438-8871 |
| Popis: | BackgroundDysphonia 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.ObjectiveThis study sought to detect various vocal fold diseases through pathological voice recognition using artificial intelligence.MethodsWe 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 network approach was applied to train the model, and findings were compared with those of human specialists.ResultsThe convolutional neural network model achieved a sensitivity of 0.66, a specificity of 0.91, and an overall accuracy of 66.9% for distinguishing normal voice, vocal atrophy, unilateral vocal paralysis, organic vocal fold lesions, and adductor spasmodic dysphonia. Compared with the accuracy of human specialists, the overall accuracy rates were 60.1% and 56.1% for the 2 laryngologists and 51.4% and 43.2% for the 2 general ear, nose, and throat doctors.ConclusionsVoice alone could be used for common vocal fold disease recognition through a deep learning approach after training with our Mandarin pathological voice database. This approach involving artificial intelligence could be clinically useful for screening general vocal fold disease using the voice. The approach includes a quick survey and a general health examination. It can be applied during telemedicine in areas with primary care units lacking laryngoscopic abilities. It could support physicians when prescreening cases by allowing for invasive examinations to be performed only for cases involving problems with automatic recognition or listening and for professional analyses of other clinical examination results that reveal doubts about the presence of pathologies. |
| Databáze: | OpenAIRE |
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