Artificial Intelligence Techniques for Prognostic and Diagnostic Assessments in Peripheral Artery Disease: A Scoping Review
Clinical Snapshot
PICO Framework
| P — Population | Patients with peripheral artery disease (PAD) or at risk of PAD |
| I — Intervention | Artificial intelligence techniques (machine learning, natural language processing, advanced imaging analysis) |
| C — Comparator | Traditional diagnostic and prognostic methods or no comparator (scoping review) |
| O — Outcomes | Diagnostic accuracy, prognostic prediction, clinical outcomes in PAD management |
Bottom Line
This scoping review examines artificial intelligence applications in peripheral artery disease diagnosis and prognosis. While PAD remains under-diagnosed with challenging prognostic assessment, AI techniques including machine learning and natural language processing show promise for improving clinical management. The review identifies significant methodological challenges including dataset variability, model complexity, and interpretability issues that limit current clinical implementation. The authors highlight the need for standardized performance metrics and better integration into clinical decision support systems. For Australian clinicians, this represents an emerging field requiring careful validation before routine clinical adoption, with particular attention to TGA regulatory requirements for AI diagnostic tools.
Key Findings
Effect Size: Not applicable - scoping review
Primary Outcome: Identification and characterization of AI techniques for PAD diagnosis and prognosis
Nnt Or Sensitivity: Performance metrics discussed but specific values not provided in abstract
Confidence Interval: Not provided
Clinical Application
Implementation challenges include model interpretability, dataset variability, and integration into clinical workflows Relevant to Australian vascular surgery and cardiology practice; TGA approval would be required for AI diagnostic tools; potential PBS implications for cost-effective screening Patients with suspected or confirmed PAD requiring diagnostic or prognostic assessment
Abstract
Peripheral artery disease (PAD) is a major public health concern worldwide, associated with high risk of mortality and morbidity related to cardiovascular and adverse limb events. Despite significant advances in both medical and interventional therapies, PAD often remains under-diagnosed, and the prognosis of patients can be difficult to predict. Artificial intelligence (AI) has brought a wide range of opportunities to improve the management of cardiovascular diseases, from advanced imaging analysis to machine-learning (ML)-based predictive models, and medical data management using natural language processing (NLP). The aim of this review is to summarize and discuss current techniques based on AI that have been proposed for the diagnosis and the evaluation of the prognosis in patients with PAD. The review focused on clinical studies that proposed AI-methods for the detection and the classification of PAD as well as studies that used AI-models to predict outcomes of patients. Through evaluation of study design, we discuss model choices including variability in dataset inputs, model complexity, interpretability, and challenges linked to performance metrics used. In the light of the results, we discuss potential interest for clinical decision support and highlight future directions for research and clinical practice.
References
- 1.Goffart, S., Delingette, H., Chierici, A., Guzzi, L., Nasr, B., Lareyre, F., & Raffort, J. (2024). Artificial Intelligence Techniques for Prognostic and Diagnostic Assessments in Peripheral Artery Disease: A Scoping Review. Angiology. https://doi.org/10.1177/00033197241310572
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