Private Facial Prediagnosis as an Edge Service for Parkinson's DBS Treatment Valuation

Jiang, Richard, Chazot, Paul, Pavese, Nicola, Crookes, Danny, Bouridane, Ahmed and Celebi, M. Emre (2022) Private Facial Prediagnosis as an Edge Service for Parkinson's DBS Treatment Valuation. IEEE Journal of Biomedical and Health Informatics, 26 (6). pp. 2703-2713. ISSN 2168-2194

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Official URL: https://doi.org/10.1109/JBHI.2022.3146369

Abstract

Facial phenotyping for medical prediagnosis has recently been successfully exploited as a novel way for the preclinical assessment of a range of rare genetic diseases, where facial biometrics is revealed to have rich links to underlying genetic or medical causes. In this paper, we aim to extend this facial prediagnosis technology for a more general dis-ease, Parkinson's Diseases (PD), and proposed an Artificial-Intelligence-of-Things (AIoT) edge-oriented privacy-preserving facial prediagnosis framework to analyze the treatment of Deep Brain Stimulation (DBS) on PD patients. In the proposed framework, 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, where partial homomorphic encryption (PHE) is leveraged to enable privacy-preserving deep facial diagnosis on encrypted facial patterns. In our experiments with a collected facial dataset from PD patients, for the first time, we proved that facial patterns could be used to evaluate the facial difference of PD patients undergoing DBS treatment. We further implemented a privacy-preserving information theoretical secure deep facial prediagnosis framework that can achieve the same accuracy as the non-encrypted one, showing the potential of our facial prediagnosis as a trust-worthy edge service for grading the severity of PD in patients.

Item Type: Article
Additional Information: Funding information: The manuscript was submitted on xx/o5/2021. This work was supported in part by the UK EPSRC under Grant EP/P009727/1, the Leverhulme Trust under Grant RF-2019-492, and the US National Science Foundation under Grant 1946391.
Uncontrolled Keywords: Facial Prediagnosis, Medical Biometrics, Edge AIoT, Electronic Health and Medical Records, Private Deep learning, Private Biometrics
Subjects: B900 Others in Subjects allied to Medicine
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: John Coen
Date Deposited: 28 Feb 2022 09:13
Last Modified: 28 Jun 2022 14:00
URI: http://nrl.northumbria.ac.uk/id/eprint/48565

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