ArticleSensors (Basel, Switzerland)2022
Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Clinical Translation of Artificial Intelligence-Driven Gait Analysis Using Plantar Pressure and Ground Reaction Force.Bioengineering (Basel, Switzerland) · 2026Review
- Efficacy and safety of Oliceridine versus Sufentanil in postoperative analgesia for burn skin grafting: a machine learning and SHAP-based cohort study.Frontiers in medicine · 2026Article
- Diagnostic Value of the Five Times Sit-to-Stand Test and Other Functional Measures in Patients with Arteriosclerosis Obliterans.Healthcare (Basel, Switzerland) · 2025Article
- Validation ofCirculation. Cardiovascular quality and outcomes · 2025Article
- Scoping Review of Machine Learning Techniques in Marker-Based Clinical Gait Analysis.Bioengineering (Basel, Switzerland) · 2025Review
- Prediction of Major Adverse Limb Events in Females with Peripheral Artery Disease using Blood-Based Biomarkers and Clinical Features.Journal of cardiovascular translational research · 2025Article
- Association between gait video information and general cardiovascular diseases: a prospective cross-sectional study.European heart journal. Digital health · 2024Article
- A machine learning algorithm for peripheral artery disease prognosis using biomarker data.iScience · 2024Article
- Diagnosis of disease affecting gait with a body acceleration-based model using reflected marker data for training and a wearable accelerometer for implementation.Scientific reports · 2024Article
- Machine learning-based gait adaptation dysfunction identification using CMill-based gait data.Frontiers in neurorobotics · 2024Article
- Artificial Intelligence in Cardiology and Atherosclerosis in the Context of Precision Medicine: A Scoping Review.Applied bionics and biomechanics · 2024Article
- Machine Learning in Vascular Medicine: Optimizing Clinical Strategies for Peripheral Artery Disease.Current cardiovascular risk reports · 2024Review
- A biomechanical perspective on walking in patients with peripheral artery disease.Vascular medicine (London, England) · 2023Review
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
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, and hip joint angles, torques, and powers, as well as ground reaction forces (GRF). ML was able to classify those with and without PAD using Neural Networks or Random Forest algorithms with 89% accuracy (0.64 Matthew's Correlation Coefficient) using all laboratory-based gait variables. Moreover, models using only GRF variables provided up to 87% accuracy (0.64 Matthew's Correlation Coefficient). These results indicate that ML models can classify those with and without PAD using gait signatures with acceptable performance. Results also show that an ML gait signature model that uses GRF features delivers the most informative data for PAD classification.
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Registered trials
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