Alessandra Scarton

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Background Diabetic foot is an invalidating complication of diabetes mellitus that can lead to foot ulceration and amputations. While experimental analyses are limited solely to measurements of interfacial variables, three-dimensional (3D) patient specific finite element models (FEMs) of the foot can provide both the interfacial pressures and insight into(More)
Background Foot ulcerations are one of the most common and invalidating complications which affect the diabetic patients [1,2]. Several two-dimensional (2D) finite element (FE) models of the foot have been developed in the last decades in order to understand what are the causes and to decrease their progress [3-5]. The aim of this work was to create four 2D(More)
Diabetes neuropathy and vasculopathy are the two major complications of diabetes mellitus, leading to diabetic foot disease, of which the worst consequences are plantar ulcers and amputations. Motor impairments like joint stiffness and loss of balance are distinctive effects of diabetes and they have been extensively explored. However, while altered muscle(More)
Plantar pressure simulation driven by integrated 3D motion capture data, using both a finite element and a discrete element model, is compared for ten healthy and ten diabetic neuropathic subjects. The simulated peak pressure deviated on average between 16.7 and 34.2% from the measured peak pressure. The error in the position of the peak pressure was on(More)
Diabetic foot is one of the most debilitating complications of diabetes and may lead to plantar ulcers. In the last decade, gait analysis, musculoskeletal modelling (MSM) and finite element modelling (FEM) have shown their ability to contribute to diabetic foot prevention and suggested that the origin of the plantar ulcers is in deeper tissue layers rather(More)
There have been many recent developments in patient-specific models with their potential to provide more information on the human pathophysiology and the increase in computational power. However they are not yet successfully applied in a clinical setting. One of the main challenges is the time required for mesh creation, which is difficult to automate. The(More)
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