ArticleComputers in biology and medicine2024
Peripheral artery disease diagnosis based on deep learning-enabled analysis of non-invasive arterial pulse waveforms.
Article in Computers in biology and medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysis.Scientific reports · 2025Pooled it
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- Machine Learning Approach for Enumeration of Circulating Cells with DiffusebioRxiv : the preprint server for biology · 2026Article
- 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
- Traditional Chinese Medicine Modernization in Diagnosis and Treatment: Utilizing Artificial Intelligence and Nanotechnology.MedComm · 2026Review
- DECODE-3DViz: Efficient WebGL-Based High-Fidelity Visualization of Large-Scale Images using Level of Detail and Data Chunk Streaming.Journal of imaging informatics in medicine · 2025Article
- Deep Learning-Enabled Diagnosis of Abdominal Aortic Aneurysm Using Pulse Volume Recording Waveforms: An In Silico Study.Sensors (Basel, Switzerland) · 2025Article
- Non-Invasive Biomarkers in the Era of Big Data and Machine Learning.Sensors (Basel, Switzerland) · 2025Review
- Optimized Lightweight Architecture for Coronary Artery Disease Classification in Medical Imaging.Diagnostics (Basel, Switzerland) · 2025Article
- Imaging Characterisation of Peripheral Artery Disease: A Scoping Review on Current Classifications and New Insights Brought by Artificial Intelligence.EJVES vascular forum · 2025Review
- Revolutionizing Cardiology through Artificial Intelligence-Big Data from Proactive Prevention to Precise Diagnostics and Cutting-Edge Treatment-A Comprehensive Review of the Past 5 Years.Diagnostics (Basel, Switzerland) · 2024Review
- Machine Learning in Vascular Medicine: Optimizing Clinical Strategies for Peripheral Artery Disease.Current cardiovascular risk reports · 2024Review
Corrections and comments
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
Abstract
This paper intends to investigate the feasibility of peripheral artery disease (PAD) diagnosis based on the analysis of non-invasive arterial pulse waveforms. We generated realistic synthetic arterial blood pressure (BP) and pulse volume recording (PVR) waveform signals pertaining to PAD present at the abdominal aorta with a wide range of severity levels using a mathematical model that simulates arterial blood circulation and arterial BP-PVR relationships. We developed a deep learning (DL)-enabled algorithm that can diagnose PAD by analyzing brachial and tibial PVR waveforms, and evaluated its efficacy in comparison with the same DL-enabled algorithm based on brachial and tibial arterial BP waveforms as well as the ankle-brachial index (ABI). The results suggested that it is possible to detect PAD based on DL-enabled PVR waveform analysis with adequate accuracy, and its detection efficacy is close to when arterial BP is used (positive and negative predictive values at 40 % abdominal aorta occlusion: 0.78 vs 0.89 and 0.85 vs 0.94; area under the ROC curve (AUC): 0.90 vs 0.97). On the other hand, its efficacy in estimating PAD severity level is not as good as when arterial BP is used (r value: 0.77 vs 0.93; Bland-Altman limits of agreement: -32%-+32 % vs -20%-+19 %). In addition, DL-enabled PVR waveform analysis significantly outperformed ABI in both detection and severity estimation. In sum, the findings from this paper suggest the potential of DL-enabled non-invasive arterial pulse waveform analysis as an affordable and non-invasive means for PAD diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.