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Full-Text Articles in Nanoscience and Nanotechnology
Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data, Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Hafizur Rahman, Basheer Qolomany, Iraklis I. Pipinos, Fadi M. Alsaleem, Sara A. Myers
Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data, Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Hafizur Rahman, Basheer Qolomany, Iraklis I. Pipinos, Fadi M. Alsaleem, Sara A. Myers
Department of Mechanical and Materials Engineering: Faculty Publications
Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, …
Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data, Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Basheer Qolomany, Iraklis I. Pipinos, Fadi Alsaleem, Sara A. Myers
Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data, Ali Al-Ramini, Mahdi Hassan, Farahnaz Fallahtafti, Mohammad Ali Takallou, Basheer Qolomany, Iraklis I. Pipinos, Fadi Alsaleem, Sara A. Myers
Department of Mechanical and Materials Engineering: Faculty Publications
Peripheral artery disease (PAD) manifests from atherosclerosis, which limits blood flow to the legs and causes changes in muscle structure and function, and in gait performance. PAD is underdiagnosed, which delays treatment and worsens clinical outcomes. To overcome this challenge, the purpose of this study is to develop machine learning (ML) models that distinguish individuals with and without PAD. This is the first step to using ML to identify those with PAD risk early. We built ML models based on previously acquired overground walking biomechanics data from patients with PAD and healthy controls. Gait signatures were characterized using ankle, knee, …
In Situ Longitudinal Pre-Stretch In The Human Femoropopliteal Artery, Alexey Kamenskiy, Andreas Seas, Grant Bowen, Paul Deegan, Anastasia Desyatova, Nick Bohlim, William Poulson, Jason N. Mactaggart
In Situ Longitudinal Pre-Stretch In The Human Femoropopliteal Artery, Alexey Kamenskiy, Andreas Seas, Grant Bowen, Paul Deegan, Anastasia Desyatova, Nick Bohlim, William Poulson, Jason N. Mactaggart
Department of Mechanical and Materials Engineering: Faculty Publications
In situ longitudinal (axial) pre-stretch (LPS) plays a fundamental role in the mechanics of the femoropopliteal artery (FPA). It conserves energy during pulsation and prevents buckling of the artery during limb movement. We investigated how LPS is affected by demographics and risk factors, and how these patient characteristics associate with the structural and physiologic features of the FPA. LPS was measured in n=148 fresh human FPAs (14–80 years old). Mechanical properties were characterized with biaxial extension and histopathological characteristics were quantified with Verhoeff-Van Gieson Staining. Constitutive modeling was used to calculate physiological stresses and stretches which were then analyzed …