Reference : Leveraging the Potential of Digital Technology for Better Individualized Treatment of...
Scientific journals : Article
Life sciences : Biotechnology
Life sciences : Multidisciplinary, general & others
Human health sciences : Neurology
Systems Biomedicine
Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease.
Fröhlich, Holger [> >]
Bontridder, Noémi [> >]
Petrovska-Delacréta, Dijana [> >]
Glaab, Enrico mailto [University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB) > Biomedical Data Science]
Kluge, Felix [> >]
Yacoubi, Mounim El [> >]
Marín Valero, Mayca [> >]
Corvol, Jean-Christophe [> >]
Eskofier, Bjoern [> >]
Van Gyseghem, Jean-Marc [> >]
Lehericy, Stepháne [> >]
Winkler, Jürgen [> >]
Klucken, Jochen []
Frontiers in neurology
Yes (verified by ORBilu)
[en] Artificial Intelligence ; Parkinson's Disease ; digital biomarker ; digital health ; precision medicine
[en] Recent years have witnessed a strongly increasing interest in digital technology within medicine (sensor devices, specific smartphone apps) and specifically also neurology. Quantitative measures derived from digital technology could provide Digital Biomarkers (DMs) enabling a quantitative and continuous monitoring of disease symptoms, also outside clinics. This includes the possibility to continuously and sensitively monitor the response to treatment, hence opening the opportunity to adapt medication pathways quickly. In addition, DMs may in the future allow early diagnosis, stratification of patient subgroups and prediction of clinical outcomes. Thus, DMs could complement or in certain cases even replace classical examiner-based outcome measures and molecular biomarkers measured in cerebral spinal fluid, blood, urine, saliva, or other body liquids. Altogether, DMs could play a prominent role in the emerging field of precision medicine. However, realizing this vision requires dedicated research. First, advanced data analytical methods need to be developed and applied, which extract candidate DMs from raw signals. Second, these candidate DMs need to be validated by (a) showing their correlation to established clinical outcome measures, and (b) demonstrating their diagnostic and/or prognostic value compared to established biomarkers. These points again require the use of advanced data analytical methods, including machine learning. In addition, the arising ethical, legal and social questions associated with the collection and processing of sensitive patient data and the use of machine learning methods to analyze these data for better individualized treatment of the disease, must be considered thoroughly. Using Parkinson's Disease (PD) as a prime example of a complex multifactorial disorder, the purpose of this article is to critically review the current state of research regarding the use of DMs, discuss open challenges and highlight emerging new directions.
Luxembourg Centre for Systems Biomedicine (LCSB): Biomedical Data Science (Glaab Group)
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students
The original publication is available at
FnR ; FNR14599012 > Enrico Glaab > DIGIPD > Validating Digital Biomarkers For Better Personalized Treatment Of Parkinson’S Disease > 01/05/2021 > 30/04/2024 > 2020

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