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

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.

Comparison of a Drug-Free Early Programmed Dismantling PDLLA Bioresorbable Scaffold and a Metallic Stent in a Porcine Coronary Artery Model at 3-Year Follow-Up.

BACKGROUND: Arterial Remodeling Technologies bioresorbable scaffold (ART-BRS), composed of l- and d-lactyl units without drug, has shown its safety in a porcine coronary model at 6 months. However, long-term performance remains unknown. The aim of this study was to evaluate the ART-BRS compared to a bare metal stent (BMS) in a healthy porcine coronary model for up to 3 years. METHODS AND RESULTS: Eighty-two ART-BRS and 66 BMS were implanted in 64 Yucatan swine, and animals were euthanatized at intervals of 1, 3, 6, 9, 12, 18, 24, and 36 months to determine the vascular response using quantitative coronary angiography, optical coherence tomography, light and scanning electron microscopy, and molecular weight analysis. Lumen enlargement was observed in ART-BRS as early as 3 months, which progressively increased up to 18 months, whereas BMS showed no significant difference over time. Percentage area stenosis by optical coherence tomography was greater in ART-BRS than in BMS at 1 and 3 months, but this relationship reversed beyond 3 months. Inflammation peaked at 6 months and thereafter continued to decrease up to 36 months. Complete re-endothelialization was observed at 1 month following implantation in both ART-BRS and BMS. Scaffold dismantling started at 3 months, which allowed early vessel enlargement, and bioresorption was complete by 24 months. CONCLUSIONS: ART-BRS has the unique quality of early programmed dismantling accompanied by vessel lumen enlargement with mild to moderate inflammation. The main distinguishing feature of the ART-BRS from other scaffolds made from poly-l-lactic acid may result in early and long-term vascular restoration.

Automated segmentation of the lamina cribrosa using Frangi's filter: a novel approach for rapid identification of tissue volume fraction and beam orientation in a trabeculated structure in the eye.

The lamina cribrosa (LC) is a tissue in the posterior eye with a complex trabecular microstructure. This tissue is of great research interest, as it is likely the initial site of retinal ganglion cell axonal damage in glaucoma. Unfortunately, the LC is difficult to access experimentally, and thus imaging techniques in tandem with image processing have emerged as powerful tools to study the microstructure and biomechanics of this tissue. Here, we present a staining approach to enhance the contrast of the microstructure in micro-computed tomography (micro-CT) imaging as well as a comparison between tissues imaged with micro-CT and second harmonic generation (SHG) microscopy. We then apply a modified version of Frangi's vesselness filter to automatically segment the connective tissue beams of the LC and determine the orientation of each beam. This approach successfully segmented the beams of a porcine optic nerve head from micro-CT in three dimensions and SHG microscopy in two dimensions. As an application of this filter, we present finite-element modelling of the posterior eye that suggests that connective tissue volume fraction is the major driving factor of LC biomechanics. We conclude that segmentation with Frangi's filter is a powerful tool for future image-driven studies of LC biomechanics.