Towards a diagnostic tool for neurological gait disorders in childhood combining 3D gait kinematics and deep learning

• Published in: Computers in biology and medicine 2024-03, Vol.171, p.108095, Article 108095
• Main Authors: Lan, Zhengyang, Lempereur, Mathieu, Gueret, Gwenael, Houx, Laetitia, Cacioppo, Marine, Pons, Christelle, Mensah, Johanne, Rémy-Néris, Olivier, Aïssa-El-Bey, Abdeldjalil, Rousseau, François, Brochard, Sylvain
• Format: Article
• Language: English
• Subjects: Abnormalities; Accuracy; Biomechanical Phenomena; Cerebral palsy; Cerebral Palsy - diagnosis; Child; Childhood; Children; Clinical gait analysis; Data analysis; Data collection; Deep Learning; Developmental disabilities; Diagnostic tool; Disorders; Engineering Sciences; Evaluation; Gait; Humans; Kinematics; Neurological diseases; Neuromuscular diseases; Neuromuscular Diseases - diagnosis; Sensitivity analysis; Standardization; Stroke; Times series classification
• ISSN: 0010-4825; 1879-0534; 1879-0534
• DOI: 10.1016/j.compbiomed.2024.108095

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. •Data-driven diagnostic models require no human intervention or expert knowledge.•The diagnostic tool distinguished different etiologies of pathological gait.•The diagnostic tool determined the onset time of stroke.•Diagnostic accuracy of deep learning-based models ranged from 0.77 to 0.99.•Deep learning extends the use of 3D gait analysis from evaluation to diagnosis.