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Wearables and AI better predict the progression of muscular dystrophy

Clinical trials in neurological diseases often involve subjective, qualitative endpoints, such ‘by eye’ observations of movement. We developed an artificial intelligence–based method to analyze natural daily behavior data from people with Duchenne muscular dystrophy, using machine-learning algorithms to accurately predict their personal disease trajectories better than conventional clinical assessments.

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Fig. 1: Overview of ethomic fingerprinting of natural movement behavior of people with DMD.

References

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This is a summary of: Ricotti, V. et al. Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy. Nat. Med. https://doi.org/10.1038/s41591-022-02045-1 (2023).

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Wearables and AI better predict the progression of muscular dystrophy. Nat Med 29, 37–38 (2023). https://doi.org/10.1038/s41591-022-02191-6

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  • DOI: https://doi.org/10.1038/s41591-022-02191-6

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