ArticleBiomedicines2021
Artificial Intelligence Identifies an Urgent Need for Peripheral Vascular Intervention by Multiplexing Standard Clinical Parameters.
Article in Biomedicines, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 15 citations in OpenAlex.
- Anti-senescence therapies: a new concept to address cardiovascular disease.Cardiovascular research · 2025Review
- Computational modeling and analysis of medical resource shortages in hospital alliance: A simulation-driven approach.PloS one · 2025Article
- Article
- Machine Learning in Vascular Medicine: Optimizing Clinical Strategies for Peripheral Artery Disease.Current cardiovascular risk reports · 2024Review
- Risk profiling in the prevention and treatment of chronic wounds using artificial intelligence.International wound journal · 2022Article
- Artificial intelligence in atherosclerotic disease: Applications and trends.Frontiers in cardiovascular medicine · 2022Review
- Prediction of 2-Year Major Adverse Limb Event-Free Survival After Percutaneous Transluminal Angioplasty and Stenting for Lower Limb Atherosclerosis Obliterans: A Machine Learning-Based Study.Frontiers in cardiovascular medicine · 2022Article
- Adherence to Antiplatelet Medications among Persistent and Non-Persistent Older Patients with Peripheral Arterial Disease.Biomedicines · 2021Article
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundPeripheral artery disease (PAD) is a significant burden, particularly among patients with severe disease requiring invasive treatment. We applied a general Machine Learning (ML) workflow and investigated if a multi-dimensional marker set of standard clinical parameters can identify patients in need of vascular intervention without specialized intra-hospital diagnostics.
methodsThis is a retrospective study involving patients with stable PAD (sPAD, Fontaine Class I and II,
resultsML has enabled a generation of an Artificial Intelligence (AI) PAD score (AI-PAD) that successfully divided sPAD from unPAD patients (high AI-PAD in sPAD, low AI-PAD in unPAD, cutoff at 50 AI-PAD units). Furthermore, the probability score positively coincided with gold-standard intra-hospital mean ankle-brachial index (ABI).
conclusionAI-based tools may be promising to enable the correct identification of patients with unstable PAD by using existing clinical information, thus supplementing clinical decision making. Additional studies in larger prospective cohorts are necessary to determine the usefulness of this approach in comparison to standard diagnostic measures.
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