Article ; Online: Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy.
2023 Volume 29, Issue 1, Page(s) 95–103
Abstract: Artificial intelligence has the potential to revolutionize healthcare, yet clinical trials in neurological diseases continue to rely on subjective, semiquantitative and motivation-dependent endpoints for drug development. To overcome this limitation, we ... ...
Abstract | Artificial intelligence has the potential to revolutionize healthcare, yet clinical trials in neurological diseases continue to rely on subjective, semiquantitative and motivation-dependent endpoints for drug development. To overcome this limitation, we collected a digital readout of whole-body movement behavior of patients with Duchenne muscular dystrophy (DMD) (n = 21) and age-matched controls (n = 17). Movement behavior was assessed while the participant engaged in everyday activities using a 17-sensor bodysuit during three clinical visits over the course of 12 months. We first defined new movement behavioral fingerprints capable of distinguishing DMD from controls. Then, we used machine learning algorithms that combined the behavioral fingerprints to make cross-sectional and longitudinal disease course predictions, which outperformed predictions derived from currently used clinical assessments. Finally, using Bayesian optimization, we constructed a behavioral biomarker, termed the KineDMD ethomic biomarker, which is derived from daily-life behavioral data and whose value progresses with age in an S-shaped sigmoid curve form. The biomarker developed in this study, derived from digital readouts of daily-life movement behavior, can predict disease progression in patients with muscular dystrophy and can potentially track the response to therapy. |
---|---|
MeSH term(s) | Humans ; Muscular Dystrophy, Duchenne/drug therapy ; Activities of Daily Living ; Cross-Sectional Studies ; Artificial Intelligence ; Bayes Theorem ; Biomarkers ; Wearable Electronic Devices |
Chemical Substances | Biomarkers |
Language | English |
Publishing date | 2023-01-19 |
Publishing country | United States |
Document type | Journal Article ; Research Support, Non-U.S. Gov't |
ZDB-ID | 1220066-9 |
ISSN | 1546-170X ; 1078-8956 |
ISSN (online) | 1546-170X |
ISSN | 1078-8956 |
DOI | 10.1038/s41591-022-02045-1 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
More links
Kategorien
In stock of ZB MED Cologne/Königswinter
Zs.A 4212: Show issues | Location: Je nach Verfügbarkeit (siehe Angabe bei Bestand) bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular Jg. 1995 - 2021: Lesesall (2.OG) ab Jg. 2022: Lesesaal (EG) |
|||
Zs.MG 39: Show issues |
Order via subito
This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.