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Lan Z, Lempereur M, Gueret G, Houx L, Cacioppo M, Pons C, Mensah J, Rémy-Néris O, Aïssa-El-Bey A, Rousseau F, Brochard S. Towards a diagnostic tool for neurological gait disorders in childhood combining 3D gait kinematics and deep learning. Comput Biol Med 2024; 171:108095. [PMID: 38350399 DOI: 10.1016/j.compbiomed.2024.108095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 01/31/2024] [Accepted: 02/01/2024] [Indexed: 02/15/2024]
Abstract
Gait abnormalities are frequent in children and can be caused by different pathologies, such as cerebral palsy, neuromuscular disease, toe walker syndrome, etc. Analysis of the "gait pattern" (i.e., the way the person walks) using 3D analysis provides highly relevant clinical information. This information is used to guide therapeutic choices; however, it is underused in diagnostic processes, probably because of the lack of standardization of data collection methods. Therefore, 3D gait analysis is currently used as an assessment rather than a diagnostic tool. In this work, we aimed to determine if deep learning could be combined with 3D gait analysis data to diagnose gait disorders in children. We tested the diagnostic accuracy of deep learning methods combined with 3D gait analysis data from 371 children (148 with unilateral cerebral palsy, 60 with neuromuscular disease, 19 toe walkers, 60 with bilateral cerebral palsy, 25 stroke, and 59 typically developing children), with a total of 6400 gait cycles. We evaluated the accuracy, sensitivity, specificity, F1 score, Area Under the Curve (AUC) score, and confusion matrix of the predictions by ResNet, LSTM, and InceptionTime deep learning architectures for time series data. The deep learning-based models had good to excellent diagnostic accuracy (ranging from 0.77 to 0.99) for discrimination between healthy and pathological gait, discrimination between different etiologies of pathological gait (binary and multi-classification); and determining stroke onset time. LSTM performed best overall. This study revealed that the gait pattern contains specific, pathology-related information. These results open the way for an extension of 3D gait analysis from evaluation to diagnosis. Furthermore, the method we propose is a data-driven diagnostic model that can be trained and used without human intervention or expert knowledge. Furthermore, the method could be used to distinguish gait-related pathologies and their onset times beyond those studied in this research.
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Affiliation(s)
- Zhengyang Lan
- Laboratoire de Traitement de l'Information Médicale INSERM U1101, Brest, France; IMT Atlantique, LaTIM U1101 INSERM, Brest, France
| | - Mathieu Lempereur
- Laboratoire de Traitement de l'Information Médicale INSERM U1101, Brest, France; Université de Bretagne Occidentale, Brest, France; CHU de Brest, Hôpital Morvan, service de médecine physique et de réadaptation, Brest, France.
| | - Gwenael Gueret
- CHU de Brest, Hôpital Morvan, service de médecine physique et de réadaptation, Brest, France
| | - Laetitia Houx
- Laboratoire de Traitement de l'Information Médicale INSERM U1101, Brest, France; Université de Bretagne Occidentale, Brest, France; CHU de Brest, Hôpital Morvan, service de médecine physique et de réadaptation, Brest, France; Fondation Ildys, Brest, France
| | - Marine Cacioppo
- Laboratoire de Traitement de l'Information Médicale INSERM U1101, Brest, France; Université de Bretagne Occidentale, Brest, France; CHU de Brest, Hôpital Morvan, service de médecine physique et de réadaptation, Brest, France
| | - Christelle Pons
- Laboratoire de Traitement de l'Information Médicale INSERM U1101, Brest, France; Université de Bretagne Occidentale, Brest, France; CHU de Brest, Hôpital Morvan, service de médecine physique et de réadaptation, Brest, France; Fondation Ildys, Brest, France
| | - Johanne Mensah
- Laboratoire de Traitement de l'Information Médicale INSERM U1101, Brest, France; Université de Bretagne Occidentale, Brest, France; CHU de Brest, Hôpital Morvan, service de médecine physique et de réadaptation, Brest, France; Fondation Ildys, Brest, France
| | - Olivier Rémy-Néris
- Laboratoire de Traitement de l'Information Médicale INSERM U1101, Brest, France; Université de Bretagne Occidentale, Brest, France; CHU de Brest, Hôpital Morvan, service de médecine physique et de réadaptation, Brest, France
| | | | - François Rousseau
- Laboratoire de Traitement de l'Information Médicale INSERM U1101, Brest, France; IMT Atlantique, LaTIM U1101 INSERM, Brest, France
| | - Sylvain Brochard
- Laboratoire de Traitement de l'Information Médicale INSERM U1101, Brest, France; Université de Bretagne Occidentale, Brest, France; CHU de Brest, Hôpital Morvan, service de médecine physique et de réadaptation, Brest, France; Fondation Ildys, Brest, France
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Winner TS, Rosenberg MC, Jain K, Kesar TM, Ting LH, Berman GJ. Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics. PLoS Comput Biol 2023; 19:e1011556. [PMID: 37889927 PMCID: PMC10610102 DOI: 10.1371/journal.pcbi.1011556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 09/30/2023] [Indexed: 10/29/2023] Open
Abstract
Locomotion results from the interactions of highly nonlinear neural and biomechanical dynamics. Accordingly, understanding gait dynamics across behavioral conditions and individuals based on detailed modeling of the underlying neuromechanical system has proven difficult. Here, we develop a data-driven and generative modeling approach that recapitulates the dynamical features of gait behaviors to enable more holistic and interpretable characterizations and comparisons of gait dynamics. Specifically, gait dynamics of multiple individuals are predicted by a dynamical model that defines a common, low-dimensional, latent space to compare group and individual differences. We find that highly individualized dynamics-i.e., gait signatures-for healthy older adults and stroke survivors during treadmill walking are conserved across gait speed. Gait signatures further reveal individual differences in gait dynamics, even in individuals with similar functional deficits. Moreover, components of gait signatures can be biomechanically interpreted and manipulated to reveal their relationships to observed spatiotemporal joint coordination patterns. Lastly, the gait dynamics model can predict the time evolution of joint coordination based on an initial static posture. Our gait signatures framework thus provides a generalizable, holistic method for characterizing and predicting cyclic, dynamical motor behavior that may generalize across species, pathologies, and gait perturbations.
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Affiliation(s)
- Taniel S. Winner
- W.H. Coulter Dept. Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Michael C. Rosenberg
- W.H. Coulter Dept. Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, United States of America
| | - Kanishk Jain
- Department of Physics, Emory University, Atlanta, Georgia, United States of America
| | - Trisha M. Kesar
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University, Atlanta, Georgia, United States of America
| | - Lena H. Ting
- W.H. Coulter Dept. Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, Georgia, United States of America
- Department of Rehabilitation Medicine, Division of Physical Therapy, Emory University, Atlanta, Georgia, United States of America
| | - Gordon J. Berman
- Department of Biology, Emory University, Atlanta, Georgia, United States of America
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