Cargando…
Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy
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 move...
Autores principales: | Ricotti, Valeria, Kadirvelu, Balasundaram, Selby, Victoria, Festenstein, Richard, Mercuri, Eugenio, Voit, Thomas, Faisal, A. Aldo |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group US
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9873561/ https://www.ncbi.nlm.nih.gov/pubmed/36658421 http://dx.doi.org/10.1038/s41591-022-02045-1 |
Ejemplares similares
-
A wearable motion capture suit and machine learning predict disease progression in Friedreich’s ataxia
por: Kadirvelu, Balasundaram, et al.
Publicado: (2023) -
The NorthStar Ambulatory Assessment in Duchenne muscular dystrophy: considerations for the design of clinical trials
por: Ricotti, Valeria, et al.
Publicado: (2016) -
Categorizing natural history trajectories of ambulatory function measured by the 6-minute walk distance in patients with Duchenne muscular dystrophy
por: Mercuri, Eugenio, et al.
Publicado: (2016) -
Growth pattern trajectories in boys with Duchenne muscular dystrophy
por: Stimpson, Georgia, et al.
Publicado: (2022) -
International workshop on assessment of upper limb function in Duchenne Muscular Dystrophy: Rome, 15–16 February 2012
por: Mercuri, Eugenio, et al.
Publicado: (2012)