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

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Private Facial Prediagnosis as an Edge Service for Parkinson's DBS Treatment Valuation. / Jiang, Richard; Chazot, Paul L; Pavese, Nicola; Crookes, Danny; Bouridane, Ahmed; Celebi, M. Emre.

In: IEEE Journal of Biomedical and Health Informatics, Vol. 26, No. 6, 30.06.2022, p. 2703-2713.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Jiang, R, Chazot, PL, Pavese, N, Crookes, D, Bouridane, A & Celebi, ME 2022, 'Private Facial Prediagnosis as an Edge Service for Parkinson's DBS Treatment Valuation', IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 6, pp. 2703-2713. https://doi.org/10.1109/JBHI.2022.3146369

APA

Jiang, R., Chazot, P. L., Pavese, N., Crookes, D., Bouridane, A., & Celebi, M. E. (2022). Private Facial Prediagnosis as an Edge Service for Parkinson's DBS Treatment Valuation. IEEE Journal of Biomedical and Health Informatics, 26(6), 2703-2713. https://doi.org/10.1109/JBHI.2022.3146369

Vancouver

Jiang R, Chazot PL, Pavese N, Crookes D, Bouridane A, Celebi ME. Private Facial Prediagnosis as an Edge Service for Parkinson's DBS Treatment Valuation. IEEE Journal of Biomedical and Health Informatics. 2022 Jun 30;26(6):2703-2713. https://doi.org/10.1109/JBHI.2022.3146369

Author

Jiang, Richard ; Chazot, Paul L ; Pavese, Nicola ; Crookes, Danny ; Bouridane, Ahmed ; Celebi, M. Emre. / Private Facial Prediagnosis as an Edge Service for Parkinson's DBS Treatment Valuation. In: IEEE Journal of Biomedical and Health Informatics. 2022 ; Vol. 26, No. 6. pp. 2703-2713.

Bibtex

@article{b4d33e26405c41e19916cfc8501fab2d,
title = "Private Facial Prediagnosis as an Edge Service for Parkinson's DBS Treatment Valuation",
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.",
author = "Richard Jiang and Chazot, {Paul L} and Nicola Pavese and Danny Crookes and Ahmed Bouridane and Celebi, {M. Emre}",
note = "{\textcopyright}2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. ",
year = "2022",
month = jun,
day = "30",
doi = "10.1109/JBHI.2022.3146369",
language = "English",
volume = "26",
pages = "2703--2713",
journal = " IEEE Journal of Biomedical and Health Informatics",
publisher = "IEEE",
number = "6",

}

RIS

TY - JOUR

T1 - Private Facial Prediagnosis as an Edge Service for Parkinson's DBS Treatment Valuation

AU - Jiang, Richard

AU - Chazot, Paul L

AU - Pavese, Nicola

AU - Crookes, Danny

AU - Bouridane, Ahmed

AU - Celebi, M. Emre

N1 - ©2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PY - 2022/6/30

Y1 - 2022/6/30

N2 - 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.

AB - 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.

U2 - 10.1109/JBHI.2022.3146369

DO - 10.1109/JBHI.2022.3146369

M3 - Journal article

VL - 26

SP - 2703

EP - 2713

JO - IEEE Journal of Biomedical and Health Informatics

JF - IEEE Journal of Biomedical and Health Informatics

IS - 6

ER -