Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease

Froehlich H, Bontridder N, Petrovska-Delacreta D, Glaab E, Kluge F, El Yacoubi M, Marin Valero M, Corvol JC, Eskofier B, Van Gyseghem JM, Lehericy S, Winkler J, Klucken J (2022)

Publication Type: Journal article, Review article

Publication year: 2022

Journal

Frontiers in Neurology Frontiers Research Foundation / Frontiers Media

Book Volume: 13

DOI: 10.3389/fneur.2022.788427

Abstract

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.

Authors with CRIS profile

Felix Kluge Machine Learning and Data Analytics Lab Björn Eskofier Machine Learning and Data Analytics Lab Jürgen Winkler Professur für Molekulare Neurologie

Involved external institutions

Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen (SCAI) DE Germany (DE) Université de Namur BE Belgium (BE) Institut Mines-Télécom (IMT) FR France (FR) University of Luxembourg LU Luxembourg (LU) Asociación Parkinson Madrid / Madrid Parkinson Association ES Spain (ES) Institut du Cerveau et de la Moelle Epinière (ICM) FR France (FR)

How to cite

APA:

Froehlich, H., Bontridder, N., Petrovska-Delacreta, D., Glaab, E., Kluge, F., El Yacoubi, M.,... Klucken, J. (2022). Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease. Frontiers in Neurology, 13. https://dx.doi.org/10.3389/fneur.2022.788427

MLA:

Froehlich, Holger, et al. "Leveraging the Potential of Digital Technology for Better Individualized Treatment of Parkinson's Disease." Frontiers in Neurology 13 (2022).

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