HeartFlow excels at providing a functional, physiological assessment of coronary blockages through its proprietary FFRct (fractional flow reserve from CT) simulation. This non-invasive technique uses computational fluid dynamics on a standard CCTA scan to model blood flow and pressure, identifying lesions that cause ischemia. For example, the PLATFORM study demonstrated a 61% reduction in unnecessary invasive angiograms when using HeartFlow FFRct, showcasing its strength in guiding revascularization decisions and its established Category I CPT reimbursement code.
Comparison
HeartFlow vs. Cleerly

Introduction
A data-driven comparison of two leading AI platforms for coronary artery disease analysis, focusing on their distinct technological approaches and clinical applications.
Cleerly takes a different approach by focusing on a detailed, AI-based quantitative plaque characterization from the same CCTA data. Its platform analyzes plaque morphology—categorizing it as non-calcified, calcified, or low-density—and provides a patient-specific CAD risk score. This results in a trade-off: while it offers superior granularity on plaque composition, a key predictor of future cardiac events, it does not directly simulate functional blood flow impact like FFRct. Its clinical utility is strongest in preventive risk stratification and treatment planning based on plaque burden.
The key trade-off: If your priority is determining the hemodynamic significance of a known stenosis to guide stenting or bypass surgery, choose HeartFlow. Its FFRct simulation is the clinical gold standard for functional assessment. If you prioritize comprehensive, preventive risk stratification and understanding plaque biology to optimize medical therapy, choose Cleerly. Its AI-powered plaque analysis provides actionable insights for long-term management. For a broader view of AI in medical imaging, explore our analysis of Zebra Medical Vision vs. Qure.ai and Arterys vs. Nanox.AI.
HeartFlow vs. Cleerly: Head-to-Head Comparison
Direct comparison of two leading AI-powered platforms for coronary artery disease (CAD) analysis, focusing on diagnostic approach, clinical utility, and commercial metrics.
| Metric / Feature | HeartFlow | Cleerly |
|---|---|---|
Primary AI Analysis | FFRct (Fractional Flow Reserve) Simulation | CCTA Plaque Characterization & Quantification |
Key Output | Ischemia-causing lesion identification (FFR value) | Plaque burden, morphology, and risk score |
Diagnostic Accuracy (vs. Invasive FFR) |
| N/A (Anatomic vs. functional assessment) |
FDA Clearance Status | 510(k) cleared for FFRct Analysis | 510(k) cleared for CCTA analysis |
Reimbursement (2026 Avg. US) | $1,450 - $1,800 per analysis | $300 - $500 per analysis |
Analysis Turnaround Time | ~4-6 hours (cloud processing) | < 5 minutes (on-site/cloud) |
Clinical Utility Focus | Guiding revascularization (PCI/CABG) decisions | Preventative risk stratification & medical therapy guidance |
Direct Integration with PACS/EHR |
TL;DR Summary
Key strengths and trade-offs for coronary artery disease (CAD) analysis at a glance.
Choose HeartFlow for Treatment Planning
Specific advantage: Provides a simulated FFRct (Fractional Flow Reserve) value from a standard CCTA scan. This quantifies the functional significance of a stenosis, predicting whether a blockage impedes blood flow. This matters for interventional cardiologists deciding between medical therapy, stenting, or bypass surgery, as it directly informs revascularization strategy.
Choose Cleerly for Plaque Characterization
Specific advantage: Uses AI to quantify and characterize plaque composition (calcified, non-calcified, low-density) and stenosis. This matters for preventive cardiologists and primary care aiming for early, aggressive medical management to stabilize plaque and prevent future cardiac events, shifting focus from reactive to preventative care.
HeartFlow's Reimbursement Edge
Specific advantage: Has established Category I CPT codes (e.g., 0551T, 0552T) for FFRct analysis, facilitating consistent insurance reimbursement. This matters for hospital administrators and radiology departments requiring predictable financial pathways to integrate advanced AI diagnostics into clinical workflow and ensure sustainability.
Cleerly's Direct Imaging Analysis
Specific advantage: Delivers a comprehensive, AI-driven CCTA report without requiring off-site supercomputing simulation, potentially offering faster turnaround. This matters for imaging centers and cardiology practices prioritizing rapid, detailed anatomic and compositional analysis to accelerate patient diagnosis and initial management discussions.
HeartFlow vs. Cleerly
HeartFlow for Treatment Planning
Verdict: The definitive choice for guiding interventional cardiology. HeartFlow's FFRct analysis provides a simulated fractional flow reserve value, a functional metric that directly indicates whether a coronary stenosis is hemodynamically significant. This is critical for deciding between medical therapy, stenting, or bypass surgery. The platform's 3D anatomical models and ischemia maps offer interventional cardiologists a precise, patient-specific roadmap for planning PCI (percutaneous coronary intervention). Its clinical utility is backed by extensive outcomes data, making it the standard for functional assessment prior to invasive procedures. For a deeper dive into AI's role in clinical decision support, see our guide on AI Medical Diagnostic and Patient Risk Platforms.
Cleerly for Treatment Planning
Verdict: Superior for comprehensive plaque characterization and risk stratification. Cleerly's AI analyzes CCTA scans to provide a quantitative, detailed breakdown of plaque composition (calcified, non-calcified, low-attenuation). This goes beyond stenosis severity to identify high-risk plaque features associated with future cardiac events. For treatment planning, this data helps clinicians understand the underlying biology of disease, informing more aggressive medical management (e.g., statin therapy) and identifying patients who may benefit from earlier intervention. Its strength lies in preventative risk assessment and guiding long-term management strategies.
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Final Verdict and Recommendation
A data-driven conclusion on choosing between HeartFlow's physiology simulation and Cleerly's plaque characterization for coronary artery disease management.
HeartFlow excels at providing a functional, physiological assessment of coronary blockages through its FFRct simulation. This non-invasive calculation of fractional flow reserve from a standard CCTA scan has demonstrated high diagnostic accuracy, with clinical studies showing a sensitivity of ~90% and specificity of ~85% for identifying hemodynamically significant lesions. This functional data is directly actionable for interventional cardiologists planning revascularization, such as stenting or bypass surgery, and is supported by established Category III CPT codes (e.g., 0501T, 0502T) for reimbursement, making it a proven tool for guiding treatment decisions.
Cleerly takes a different, more granular approach by using AI to perform quantitative plaque analysis on CCTA images. Its strength lies in characterizing plaque composition—differentiating between calcified, non-calcified, and low-attenuation plaque—and providing a comprehensive, standardized Atherosclerosis Severity Score. This results in a trade-off: while it offers unparalleled detail on plaque burden and vulnerability, which is critical for long-term risk stratification and preventative medical therapy optimization, its direct link to immediate procedural planning is less established than FFRct. Its reimbursement pathway often relies on broader CCTA billing codes supplemented by AI analysis.
The key trade-off centers on clinical intent and workflow integration. For a hospital or health system where the primary need is to triage patients for the cath lab and plan specific interventions, HeartFlow's FFRct provides the definitive functional data clinicians trust. Its integration into the diagnostic pathway directly reduces unnecessary invasive angiograms. Conversely, for a preventative cardiology practice, clinical trial site, or research institution focused on comprehensive risk assessment, longitudinal monitoring of plaque progression, and tailoring intensive medical therapy, Cleerly's detailed plaque quantification is the superior tool. It aligns with the 2026 shift toward preventative healthcare by identifying high-risk plaque phenotypes before they cause events.
Consider HeartFlow if your priority is high-stakes, procedural decision-support with a clear reimbursement model for functional ischemia assessment. Choose Cleerly when your goal is advanced, AI-powered phenotyping for personalized risk stratification and medical management within a preventative care paradigm. For a broader view of how AI is transforming diagnostic workflows, see our comparison of Aidoc vs. Viz.ai for radiology triage and the role of AI governance platforms in ensuring the safe deployment of these clinical tools.
HeartFlow vs. Cleerly: Core Technology & Clinical Use Case Fit
A direct comparison of the two leading AI-powered coronary artery disease (CAD) analysis platforms, highlighting their distinct technological approaches and the clinical scenarios where each excels.
HeartFlow FFRct: Gold-Standard Physiology
Non-invasive FFR simulation: Creates a patient-specific 3D model to compute fractional flow reserve (FFR) from a standard CCTA scan. This provides a physiological assessment of blood flow, identifying which blockages are hemodynamically significant (FFR ≤0.80). This is critical for treatment planning, helping interventional cardiologists decide if a patient needs stenting, bypass, or just medication.
HeartFlow: Strong Reimbursement Pathway
Established CPT codes: HeartFlow FFRct analysis has dedicated Category I CPT codes (e.g., 0501T, 0502T), facilitating predictable insurance reimbursement in the U.S. This reduces financial uncertainty for hospitals and imaging centers, making it a more viable integrated service within existing cardiology workflows and a key factor for enterprise adoption.
Cleerly: Unmatched Plaque Characterization
AI-based CCTA plaque analysis: Goes beyond stenosis to quantify and characterize plaque composition (calcified, non-calcified, low-attenuation). This provides a morphological assessment of disease burden and identifies high-risk plaque features associated with future cardiac events. This matters for risk stratification and guiding aggressive preventive therapy beyond just treating flow-limiting lesions.
Cleerly: Direct, Actionable Quantification
Provides absolute volumes and percentages for each plaque type, offering quantitative, reproducible metrics for tracking disease progression or regression over time. This supports a preventative care model by enabling clinicians to monitor the impact of lifestyle or pharmaceutical interventions. It's particularly valuable for clinical trials and managing patients with non-obstructive CAD who are still at risk.
HeartFlow Limitation: Anatomical Prerequisites
Requires high-quality CCTA scans with specific anatomical coverage and contrast timing. Scans with excessive motion, severe calcification (blooming artifact), or poor signal-to-noise ratio may be unsuitable for analysis, leading to indeterminate results. This can limit its applicability in a broader, real-world patient population and may necessitate repeat imaging.
Cleerly Limitation: Evolving Reimbursement
Lacks dedicated CPT codes as of 2026, typically billed under broader CCTA analysis codes. This creates reimbursement variability and potential administrative burden for providers. While its clinical utility is recognized, the financial model is less mature than HeartFlow's, which can be a significant barrier to widespread hospital-scale deployment and affects total cost of ownership calculations.

About the author
Prasad Kumkar
CEO & MD, Inference Systems
Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.
His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.
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