Abstract:
Facial phenotyping for medical prediagnosis has been successfully used to preclinically assess a variety of rare genetic diseases. Facial biometrics has rich links to underlying genetic or medical causes. This paper extends facial prediagnosis technology to Parkinson’s Disease (PD) and proposes an Artificial-Intelligence-of-Things (AIoT) edge-oriented privacy-preserving framework to analyze Deep Brain Stimulation (DBS) treatment on PD patients. A novel edge-based privacy-preserving framework is proposed to implement private deep facial diagnosis as a service over an AIoT-oriented information theoretically secure multi-party communication scheme. Data privacy has been a primary concern toward a wider exploitation of Electronic Health and Medical Records (EHR/EMR) over cloud-based medical services. For the first time, we used facial patterns to assess PD patients’ facial differences after DBS treatment. We implemented a privacy-preserving information theoretical secure deep facial prediagnosis framework that matches the accuracy of the non-encrypted one, demonstrating the potential of our facial prediagnosis as a trustworthy edge service for grading PD severity in patients.
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