ArticleScientific reports2022
Performance and usability testing of an automated tool for detection of peripheral artery disease using electronic health records.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06505317 (Artificial Intelligence for Early Detection of Peripheral Artery Disease), which is not on this map. Cited by 21 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Artificial Intelligence for Early Detection of Peripheral Artery Disease
Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnosis models to predict peripheral arterial disease: a systematic review and meta analysis.Scientific reports · 2025Pooled it
- Performance and Fairness of a Peripheral Artery Disease Detection Model Across Patient Phenotypes and Health Systems.medRxiv : the preprint server for health sciences · 2026Article
- Artificial Intelligence in Peripheral Artery Disease: A Science Advisory From the American Heart Association.Circulation. Population health and outcomes · 2026Review
- Extending the Observational Medical Outcomes Partnership Common Data Model to Support Observational Peripheral Vascular Disease Research.The Journal of surgical research · 2026Observational
- Clinically-guided models or foundation models? predicting cervical spondylotic myelopathy from electronic health records.NPJ digital medicine · 2026Article
- Public health intelligence-ready blueprint for strengthening community resilience: a multi-stakeholder qualitative study in tertiary Chinese hospitals.Frontiers in public health · 2026Article
- Peripheral Artery Disease in US Primary Care Practices: A Retrospective EHR-Based Analysis from 2018 to 2022.Journal of general internal medicine · 2025Article
- A Comprehensive Comparison and Evaluation of AI-Powered Healthcare Mobile Applications' Usability.Healthcare (Basel, Switzerland) · 2025Article
- 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
- Gastric Cancer Detection with Ensemble Learning on Digital Pathology: Use Case of Gastric Cancer on GasHisSDB Dataset.Diagnostics (Basel, Switzerland) · 2024Article
- End Users' Perceived Engagement with Clinical Dashboards: A Rapid Review.Studies in health technology and informatics · 2024Review
- Article
- Machine Learning in Vascular Medicine: Optimizing Clinical Strategies for Peripheral Artery Disease.Current cardiovascular risk reports · 2024Review
- Clinical use of polygenic risk scores for detection of peripheral artery disease and cardiovascular events.PloS one · 2024Article
- A novel recommender framework with chatbot to stratify heart attack risk.Discover medicine · 2024Article
- Article
- Article
- Artificial intelligence in clinical workflow processes in vascular surgery and beyond.Seminars in vascular surgery · 2023Review
- APLUS: A Python library for usefulness simulations of machine learning models in healthcare.Journal of biomedical informatics · 2023Article
- Leveraging A Clinical Dashboard and Process Mappings to Improve Treatment Access and Outcomes for Women Veterans with Urinary Incontinence.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Peripheral artery disease (PAD) is a common cardiovascular disorder that is frequently underdiagnosed, which can lead to poorer outcomes due to lower rates of medical optimization. We aimed to develop an automated tool to identify undiagnosed PAD and evaluate physician acceptance of a dashboard representation of risk assessment. Data were derived from electronic health records (EHR). We developed and compared traditional risk score models to novel machine learning models. For usability testing, primary and specialty care physicians were recruited and interviewed until thematic saturation. Data from 3168 patients with PAD and 16,863 controls were utilized. Results showed a deep learning model that utilized time engineered features outperformed random forest and traditional logistic regression models (average AUCs 0.96, 0.91 and 0.81, respectively), P < 0.0001. Of interviewed physicians, 75% were receptive to an EHR-based automated PAD model. Feedback emphasized workflow optimization, including integrating risk assessments directly into the EHR, using dashboard designs that minimize clicks, and providing risk assessments for clinically complex patients. In conclusion, we demonstrate that EHR-based machine learning models can accurately detect risk of PAD and that physicians are receptive to automated risk detection for PAD. Future research aims to prospectively validate model performance and impact on patient outcomes.
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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.