Artificial Intelligent (AI) Clinical Edge for Voice Disorder Detection
Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 2
Yaxin Bi, Rahul Bhatia, Supriya Kapoor
Voice disorders are a widespread and significant health problem. In the United States, estimates of prevalence range from 3% to 7% of the general population and the number increases significantly in the rural US areas due to lack of availability of highly trained medical professionals and access to specialty voice care centers. Untreated, the voice disorders cost billions of dollars in lost productivity and much of the cost is paid by the tax payers. Early identification, prognosis, is a game changer as it provides a clinical pathway to reduce the impact of the disease. The goal of this paper is to develop artificial intelligent diagnostic tool that can detect voice disorders in clinical and outpatient settings through the application of advanced machine learning and neural networks techniques. Our innovation is to democratize diagnostic tool so that the disparity of access in rural areas can be reduced by bridging the gap between access and availability of specialty care through data science and machine learning.
Far Eastern Memorial Hospital
CNN, FEMH, Mel frequency spectrum, MFCC, Neural networks, RNN, Tensor flow, Voice data
Jaya Shankar Vuppalapati, Santosh Kedaru, Sharat Kedari, Anitha Ilapakurti, and Chandrasekar Vuppalapati. "Artificial Intelligent (AI) Clinical Edge for Voice Disorder Detection" Intelligent Systems and Applications: Proceedings of the 2019 Intelligent Systems Conference (IntelliSys) Volume 2 (2020): 750-766. https://doi.org/10.1007/978-3-030-29513-4_56