Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones

Abstract The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural...

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Autores principales:	Andrew P. Creagh, Florian Lipsmeier, Michael Lindemann, Maarten De Vos
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/ef240cad88a843779cae0ef61ba5cb57
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spelling	oai:doaj.org-article:ef240cad88a843779cae0ef61ba5cb572021-12-02T18:30:45ZInterpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones10.1038/s41598-021-92776-x2045-2322https://doaj.org/article/ef240cad88a843779cae0ef61ba5cb572021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-92776-xhttps://doaj.org/toc/2045-2322Abstract The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8–15%. A lack of transparency of “black-box” deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.Andrew P. CreaghFlorian LipsmeierMichael LindemannMaarten De VosNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Andrew P. Creagh Florian Lipsmeier Michael Lindemann Maarten De Vos Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones
description	Abstract The emergence of digital technologies such as smartphones in healthcare applications have demonstrated the possibility of developing rich, continuous, and objective measures of multiple sclerosis (MS) disability that can be administered remotely and out-of-clinic. Deep Convolutional Neural Networks (DCNN) may capture a richer representation of healthy and MS-related ambulatory characteristics from the raw smartphone-based inertial sensor data than standard feature-based methodologies. To overcome the typical limitations associated with remotely generated health data, such as low subject numbers, sparsity, and heterogeneous data, a transfer learning (TL) model from similar large open-source datasets was proposed. Our TL framework leveraged the ambulatory information learned on human activity recognition (HAR) tasks collected from wearable smartphone sensor data. It was demonstrated that fine-tuning TL DCNN HAR models towards MS disease recognition tasks outperformed previous Support Vector Machine (SVM) feature-based methods, as well as DCNN models trained end-to-end, by upwards of 8–15%. A lack of transparency of “black-box” deep networks remains one of the largest stumbling blocks to the wider acceptance of deep learning for clinical applications. Ensuing work therefore aimed to visualise DCNN decisions attributed by relevance heatmaps using Layer-Wise Relevance Propagation (LRP). Through the LRP framework, the patterns captured from smartphone-based inertial sensor data that were reflective of those who are healthy versus people with MS (PwMS) could begin to be established and understood. Interpretations suggested that cadence-based measures, gait speed, and ambulation-related signal perturbations were distinct characteristics that distinguished MS disability from healthy participants. Robust and interpretable outcomes, generated from high-frequency out-of-clinic assessments, could greatly augment the current in-clinic assessment picture for PwMS, to inform better disease management techniques, and enable the development of better therapeutic interventions.
format	article
author	Andrew P. Creagh Florian Lipsmeier Michael Lindemann Maarten De Vos
author_facet	Andrew P. Creagh Florian Lipsmeier Michael Lindemann Maarten De Vos
author_sort	Andrew P. Creagh
title	Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones
title_short	Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones
title_full	Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones
title_fullStr	Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones
title_full_unstemmed	Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones
title_sort	interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ef240cad88a843779cae0ef61ba5cb57
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Interpretable deep learning for the remote characterisation of ambulation in multiple sclerosis using smartphones

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