Abstract
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.
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Notes
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THE PROBLEM OF LIMITED HEALTH CARE COVERAGE VOICE DISORDER TREATMENT [TRANSCRIPT] - https://blog.asha.org/the-problem-of-limited-health-care-coverage-voice-disorder-treatment-transcript/.
- 2.
Chennai doctors help patients find their voice - https://timesofindia.indiatimes.com/city/chennai/Chennai-doctors-help-patients-find-their-voice/articleshow/16886456.cms.
- 3.
Leading causes of death in Rural America - http://www.ncsl.org/research/health/leading-causes-of-death-in-rural-america-postcard.aspx.
- 4.
LibROSA Library - https://librosa.github.io/librosa/.
- 5.
Voice disorder ICD Codes - https://www.icd10data.com/ICD10CM/Codes/R00-R99/R47-R49/R49-.
- 6.
J. Deep Learning in Python by Francois Chollet.
- 7.
TensorFlow Workflow - https://www.tensorflow.org/lite/guide/get_started.
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Acknowledgment
We sincerely thank you to the team in Far Eastern Memorial Hospital (FEMH) for providing valuable voice data without the development of Neural Network is impossible. We acknowledge and sincerely credit the support of FEMH.
Additionally, we would thank the management of Hanumayamma Innovations and Technologies, Inc., for active support they provided in helping and providing resources needed to work on the challenge. We have tested and deployed voice disorder diagnostic mobile app with Sanjeevani Electronic Health Records (www.sanjeevani-ehr.com). We have several Senior Citizen Users that are using the system and providing valuable healthcare data (see Fig. 12).
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Vuppalapati, J.S., Kedaru, S., Kedari, S., Ilapakurti, A., Vuppalapati, C. (2020). Artificial Intelligent (AI) Clinical Edge for Voice Disorder Detection. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_56
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