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Real-Time Peripheral Revascularization Planning in Chronic Limb Threatening Ischemia Using HarVI: A Digital Twin
Cyrus Tanade1, Christopher W Jensen2, Guinevere Ferreira1
1Department of Biomedical Engineering, Duke University, 534 Research Dr., Durham, NC, 27705, USA.
Insights
A new digital twin, HarVI, rapidly predicts blood flow after peripheral artery interventions. This tool aids surgeons in real-time decision-making for limb salvage in chronic limb-threatening ischemia (CLTI) patients.
Area of Science:
- Vascular Surgery
- Medical Imaging
- Computational Fluid Dynamics
Background:
- Peripheral artery disease (PAD) and chronic limb-threatening ischemia (CLTI) pose significant global health challenges, often leading to limb loss.
- Current image-guided endovascular interventions lack real-time, patient-specific hemodynamic outcome prediction tools for intraoperative planning.
- Existing computational fluid dynamics (CFD) methods are too slow for interactive surgical guidance.
Purpose of the Study:
- To extend the HarVI (HARVEY Virtual Intervention) digital twin framework to peripheral circulation.
- To evaluate HarVI's capability for real-time prediction of postoperative blood flow in superficial femoral artery (SFA) lesions.
- To assess HarVI's potential for guiding revascularization decisions in PAD patients.
Main Methods:
- Integration of 1D CFD with machine learning within the HarVI framework for rapid patient-specific analysis.
- Automated boundary condition tuning and simulation of various endovascular interventions using a machine-learned surrogate model.
- Validation of predicted hemodynamics against clinical duplex ultrasound measurements in a cohort of seven SFA disease patients.
Main Results:
- HarVI accurately predicted postoperative peak systolic velocities and replicated 1D CFD results.
- The machine-learned surrogate model provided rapid assessment of intervention scenarios, closely matching high-fidelity simulations.
- Near-real-time evaluation of treatment options was enabled, supporting intraoperative planning.
Conclusions:
- HarVI demonstrates significant promise as a digital twin platform for real-time, patient-specific intervention planning in PAD.
- The framework facilitates rapid, data-driven prediction of postoperative hemodynamics.
- HarVI has the potential to enhance intraoperative decision support and improve revascularization outcomes for CLTI patients.
Background:
Peripheral artery disease (PAD) is a leading cause of limb loss and morbidity worldwide, with chronic limb-threatening ischemia (CLTI) representing its most severe presentation. Although image-guided endovascular interventions are routinely performed, clinicians currently lack tools that provide real-time, patient-specific predictions of hemodynamic outcomes to guide revascularization decisions. Existing computational fluid dynamics (CFD) approaches can recover pre-operative hemodynamics but are typically too slow or insufficiently integrated into clinical workflows to support interactive, intraoperative planning.
Methods:
We extend HarVI (HARVEY Virtual Intervention), a previously established digital twin framework, to the peripheral circulation and evaluate its use for real-time prediction of postoperative blood flow in patients with superficial femoral artery (SFA) lesions. HarVI integrates one-dimensional CFD with machine learning to enable rapid assessment of patient-specific revascularization strategies. Key components include: (1) automated boundary condition tuning using patient-averaged and optimization-based approaches; (2) simulation of a wide range of endovascular interventions via a machine-learned surrogate model; and (3) validation of predicted postoperative hemodynamics against clinical duplex ultrasound measurements. Performance was evaluated retrospectively in a cohort of seven patients with SFA disease.
Results:
HarVI accurately predicted postoperative peak systolic velocities and reproduced full 1D CFD results across a synthetic revascularization landscape. Surrogate model predictions closely matched high-fidelity simulations while enabling rapid exploration of intervention scenarios, supporting near-real-time evaluation of treatment options.
Conclusions:
These results establish HarVI as a promising digital twin platform for real-time, patient-specific intervention planning in PAD. By enabling rapid, data-driven prediction of postoperative hemodynamics, HarVI opens the door to interactive intraoperative decision support with the potential to improve revascularization outcomes in patients with CLTI.
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