Academic Journal
Wearable full-body motion tracking of daily-life activities predicts disease trajectory in Duchenne Muscular Dystrophy
| العنوان: | Wearable full-body motion tracking of daily-life activities predicts disease trajectory in Duchenne Muscular Dystrophy |
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| المؤلفون: | Ricotti, V, Balasundaram, K, Victoria, S, Festenstein, R, Eugenio, M, Thomas, V, Faisal, A |
| المصدر: | 103 ; 95 |
| بيانات النشر: | Nature Research |
| سنة النشر: | 2022 |
| المجموعة: | Imperial College London: Spiral |
| الوصف: | Artificial intelligence has the potential to revolutionize health care, 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 digital readout of whole-body movement behaviour of Duchenne muscular dystrophy patients (n=21) and age-matched controls (n=17). Movement behaviour was assessed while the participant engaged in everyday activities using a 17-sensor body suit during 3 clinical visits over the course of 12 months. We first defined novel movement behavioural fingerprints capable of distinguishing DMD from controls. Then, we used machine learning algorithms that combined the behavioural fingerprints to make cross-sectional and longitudinal disease course predictions, which out-performed predictions derived from currently used clinical assessments. Finally, using Bayesian Optimization, we constructed a behavioural biomarker, termed the KineDMD ethomic biomarker, that is derived from daily-life behavioural data and whose value progresses with age in an S-shaped sigmoid curve form. By combining an approach that embraces daily life movement motor behaviour with machine learning, our biomarker provides a potential pathway for determining when a new therapy effect occurs or weans off. |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | unknown |
| تدمد: | 1078-8956 |
| Relation: | Nature Medicine; http://hdl.handle.net/10044/1/100028 |
| DOI: | 10.1038/s41591-022-02045-1 |
| الاتاحة: | http://hdl.handle.net/10044/1/100028 https://doi.org/10.1038/s41591-022-02045-1 |
| Rights: | © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. ; http://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: | edsbas.BA15A27B |
| قاعدة البيانات: | BASE |
| تدمد: | 10788956 |
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| DOI: | 10.1038/s41591-022-02045-1 |