Imperial College London
Portrait Picture

Professor Aldo Faisal

Faculty of Engineering, Department of Bioengineering

Professor of AI & Neuroscience
 
 
 
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Contact

 

+44 (0)20 7594 6373a.faisal Website

 
 
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Assistant

 

Miss Teresa Ng +44 (0)20 7594 8300

 
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Location

 

4.08Royal School of MinesSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Kadirvelu:2023:10.1038/s41591-022-02159-6,
author = {Kadirvelu, B and Gavriel, C and Nageshwaran, S and Chan, JPK and Nethisinghe, S and Athanasopoulos, S and Ricotti, V and Voit, T and Giunti, P and Festenstein, R and Faisal, AA},
doi = {10.1038/s41591-022-02159-6},
journal = {Nature Medicine},
pages = {86--94},
title = {A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia.},
url = {http://dx.doi.org/10.1038/s41591-022-02159-6},
volume = {29},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Friedreich's ataxia (FA) is caused by a variant of the Frataxin (FXN) gene, leading to its downregulation and progressively impaired cardiac and neurological function. Current gold-standard clinical scales use simplistic behavioral assessments, which require 18- to 24-month-long trials to determine if therapies are beneficial. Here we captured full-body movement kinematics from patients with wearable sensors, enabling us to define digital behavioral features based on the data from nine FA patients (six females and three males) and nine age- and sex-matched controls, who performed the 8-m walk (8-MW) test and 9-hole peg test (9 HPT). We used machine learning to combine these features to longitudinally predict the clinical scores of the FA patients, and compared these with two standard clinical assessments, Spinocerebellar Ataxia Functional Index (SCAFI) and Scale for the Assessment and Rating of Ataxia (SARA). The digital behavioral features enabled longitudinal predictions of personal SARA and SCAFI scores 9 months into the future and were 1.7 and 4 times more precise than longitudinal predictions using only SARA and SCAFI scores, respectively. Unlike the two clinical scales, the digital behavioral features accurately predicted FXN gene expression levels for each FA patient in a cross-sectional manner. Our work demonstrates how data-derived wearable biomarkers can track personal disease trajectories and indicates the potential of such biomarkers for substantially reducing the duration or size of clinical trials testing disease-modifying therapies and for enabling behavioral transcriptomics.
AU  - Kadirvelu,B
AU  - Gavriel,C
AU  - Nageshwaran,S
AU  - Chan,JPK
AU  - Nethisinghe,S
AU  - Athanasopoulos,S
AU  - Ricotti,V
AU  - Voit,T
AU  - Giunti,P
AU  - Festenstein,R
AU  - Faisal,AA
DO  - 10.1038/s41591-022-02159-6
EP  - 94
PY  - 2023///
SN  - 1078-8956
SP  - 86
TI  - A wearable motion capture suit and machine learning predict disease progression in Friedreich's ataxia.
T2  - Nature Medicine
UR  - http://dx.doi.org/10.1038/s41591-022-02159-6
UR  - https://www.ncbi.nlm.nih.gov/pubmed/36658420
UR  - http://hdl.handle.net/10044/1/99760
VL  - 29
ER  -
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