ArticleFrontiers in bioengineering and biotechnology2020
Detection and Severity Assessment of Peripheral Occlusive Artery Disease via Deep Learning Analysis of Arterial Pulse Waveforms: Proof-of-Concept and Potential Challenges.
Article in Frontiers in bioengineering and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 26 citations in OpenAlex.
- Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.Circulation. Population health and outcomes · 2026Review
- Deep-learning-based diagnostic models for transcutaneous oxygen pressure-defined peripheral arterial disease and lower extremity wound healing in patients with diabetic foot.BMC medical informatics and decision making · 2026Article
- Convolutional Neural Networks to Study Contrast-Enhanced Magnetic Resonance Imaging-Based Skeletal Calf Muscle Perfusion in Peripheral Artery Disease.The American journal of cardiology · 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
- Peripheral artery disease diagnosis based on deep learning-enabled analysis of non-invasive arterial pulse waveforms.Computers in biology and medicine · 2024Article
- Computer Science meets Vascular Surgery: Keeping a pulse on artificial intelligence.Seminars in vascular surgery · 2023Review
- Machine Learning-Based Peripheral Artery Disease Identification Using Laboratory-Based Gait Data.Sensors (Basel, Switzerland) · 2022Article
- Current applications of artificial intelligence in vascular surgery.Seminars in vascular surgery · 2021Review
- Review
- Deep Learning-Based Diagnosis of Peripheral Artery Disease via Continuous Property-Adversarial Regularization: Preliminary in Silico Study.IEEE access : practical innovations, open solutions · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Toward the ultimate goal of affordable and non-invasive screening of peripheral occlusive artery disease (PAD), the objective of this work is to investigate the potential of deep learning-based arterial pulse waveform analysis in detecting and assessing the severity of PAD. Using an established transmission line model of arterial hemodynamics, a large number of virtual patients associated with PAD of a wide range of severity and the corresponding arterial pulse waveform data were created. A deep convolutional neural network capable of detecting and assessing the severity of PAD based on the analysis of brachial and ankle arterial pulse waveforms was constructed, evaluated for efficacy, and compared with the state-of-the-art ankle-brachial index (ABI) using the virtual patients. The results suggested that deep learning may diagnose PAD more accurately and robustly than ABI. In sum, this work demonstrates the initial proof-of-concept of deep learning-based arterial pulse waveform analysis for affordable and convenient PAD screening as well as presents challenges that must be addressed for real-world clinical applications.
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