Cleerly is preparing to present primary results from PARAMOUNT, a randomised clinical strategy trial evaluating whether artificial intelligence-based quantitative coronary CT analysis can improve the management of patients with stable chest pain and suspected coronary artery disease. Announced on 5 November 2026, the planned late-breaking presentation will take place at the American Heart Association Scientific Sessions in Chicago on 7 November, with Cleerly identifying 530 randomised patients across 21 United States centres in the analysis being presented.
The trial compares a management approach informed by Cleerly’s AI-QCT technologies with usual clinical care over 180 days. Unlike studies that primarily examine whether imaging software accurately measures coronary plaque, PARAMOUNT is designed to investigate whether quantitative information about coronary atherosclerosis and estimated ischaemia can help clinicians make more appropriate diagnostic and treatment decisions.
The clinical significance will depend on the actual trial results, which remain under embargo until the scheduled presentation. As of 6 November, Cleerly has not disclosed whether the study met its primary endpoint, whether patients received different treatments or whether AI-guided management produced measurable clinical advantages.
The forthcoming results arrive as the company expands its FDA-cleared cardiovascular imaging portfolio and gains broader reimbursement recognition for coronary plaque analysis. Together, these developments create an important test of whether analytical capabilities can translate into clinical utility and sustainable adoption.
Why is PARAMOUNT an important test of artificial intelligence in coronary artery disease management?
Coronary artery disease develops when atherosclerotic plaque accumulates within the arteries supplying blood to the heart. Patients with stable chest pain may undergo a combination of clinical assessment, functional testing and anatomical imaging to determine whether coronary disease is present and which management strategy is appropriate.
Coronary computed tomography angiography provides noninvasive visualisation of coronary anatomy and can identify plaque and narrowing of the arteries. AI-based quantitative analysis can potentially add information about plaque burden, composition and the anatomical extent of disease.
Cleerly’s platform is designed to convert coronary CT images into structured measurements that clinicians can use when assessing disease severity. This approach aims to supplement conventional interpretation by providing quantitative information about atherosclerosis.
However, more detailed diagnostic information does not automatically produce better treatment. A patient may receive additional imaging measurements without any meaningful change in medication, follow-up or the decision to undertake an invasive procedure.
PARAMOUNT addresses this distinction by comparing different clinical management strategies rather than evaluating software measurements in isolation.
The intervention uses Cleerly’s AI-based coronary plaque and ischaemia assessment tools to inform medical and interventional decisions. The comparison group receives usual care, allowing investigators to examine whether the additional information changes how patients are managed.
This is an important development in cardiovascular artificial intelligence research because successful technical validation is only one step towards demonstrating clinical value. Hospitals and payers increasingly need evidence that software-generated information supports decisions that are appropriate, reproducible and beneficial to patients.
The company describes PARAMOUNT as the first randomised trial comparing AI-QCT-guided personalised care with usual care in this symptomatic population. The publication of its results will help establish whether that proposed management strategy offers advantages beyond conventional assessment.
How was the PARAMOUNT trial designed to compare AI-guided plaque management with usual care?
PARAMOUNT is a prospective, randomised, open-label clinical strategy study involving adults with symptoms suggestive of coronary artery disease. Cleerly’s November announcement states that the forthcoming presentation covers 530 randomised patients from 21 United States sites, with clinical management assessed over 180 days.
The trial is registered as NCT06713239, with the broader registration describing a planned enrolment of 1,000 patients and a larger listed site network. These figures should not be treated as interchangeable: the November announcement identifies the population associated with the scheduled primary-results presentation, while the registry reflects the broader registered study framework.
The experimental strategy involves coronary CT angiography followed by AI-enabled quantitative analysis of plaque morphology, composition and disease characteristics. Treating clinicians can use those findings when making medical and interventional decisions.
Patients assigned to usual care follow conventional clinical management pathways, including diagnostic assessment and subsequent treatment decisions informed by their healthcare providers.
The registered study description identifies several objectives relevant to clinical management. These include greater certainty concerning the presence of coronary artery disease, improved management of cardiovascular risk factors and more efficient referral for invasive coronary angiography and potential percutaneous coronary intervention.
The registry describes a hierarchical primary endpoint using a win-ratio analytical approach. This method compares outcomes according to a prespecified hierarchy, allowing investigators to evaluate several management-related measures within a structured statistical framework.
The precise interpretation of the primary result will require examination of the complete endpoint hierarchy, the observed differences between groups and the associated statistical analysis.
An open-label design is practical for a trial in which clinicians know what diagnostic information they have received. However, knowledge of treatment assignment can influence management decisions, particularly when outcomes include diagnostic certainty or the selection of additional testing.
The randomised design helps reduce differences in baseline patient characteristics between study groups, but it does not eliminate every potential source of bias associated with physician decision-making.
Therefore, the trial’s analytical methods and the consistency of findings across individual endpoints will be important when interpreting the presentation.
What can Cleerly PLAQUE and ISCHEMIA reveal that conventional coronary CT interpretation may miss?
Cleerly PLAQUE uses artificial intelligence to quantify and characterise atherosclerotic plaque from coronary CT angiography images. The software provides measurements intended to support assessment of coronary disease burden and characteristics that may not be captured fully by reporting the severity of arterial narrowing alone.
This distinction is clinically relevant because coronary disease is not defined exclusively by whether an individual artery contains a severe obstruction. Patients can have extensive atherosclerosis without a single critical narrowing, while plaque composition and distribution can provide additional information about disease characteristics.
Cleerly ISCHEMIA adds an assessment intended to support evaluation of the physiological significance of coronary disease. The combined approach is designed to provide clinicians with anatomical and functional information that may inform decisions about medical treatment and invasive investigation.
The underlying concept is that treatment should be informed by the broader burden and characteristics of disease rather than a narrow focus on individual stenoses.
However, the availability of more measurements creates additional interpretative responsibilities. Clinicians must determine which findings are clinically actionable and whether they justify changing treatment.
A patient with quantitatively identified plaque may require careful cardiovascular risk management, but software detection alone does not establish that a particular medicine or invasive procedure will improve that patient’s outcome.
Likewise, a physiological estimate derived from imaging must be understood within its validated intended use and interpreted alongside symptoms, clinical history and other available findings.
PARAMOUNT is intended to examine whether combining this information into a management strategy produces advantages over usual care.
For hospitals, the potential value lies not simply in creating a more detailed imaging report but in supporting decisions about preventive treatment, further investigation and the appropriate use of invasive procedures.
How does Cleerly’s latest FDA clearance strengthen its position before the PARAMOUNT results?
Cleerly announced on 30 October 2026 that the FDA had cleared an updated version of Cleerly LABS and its latest Cleerly PLAQUE algorithm. The company said the updated algorithm incorporates training information from the CONFIRM2 registry, which includes 6,550 individuals from 46 countries and approximately 50% female participation.
The expanded dataset is intended to support algorithm performance across a more diverse patient population. This is relevant because artificial intelligence models trained on narrow or unrepresentative datasets may demonstrate different performance when used across broader clinical populations.
FDA records also show a clearance decision for an updated Cleerly ISCHEMIA submission dated 29 October 2026. These regulatory developments support the company’s commercial software portfolio, although individual device clearances must be understood according to their specified intended uses.
The regulatory status should not be confused with the clinical questions being examined in PARAMOUNT. FDA 510(k) clearance establishes that a device satisfies the applicable substantial-equivalence requirements, not that an AI-guided management strategy has been demonstrated to improve cardiovascular outcomes.
Similarly, the expanded CONFIRM2 training dataset may support broader applicability, but demographic diversity alone does not guarantee equivalent performance across all populations or clinical environments.
The company expects its updated Cleerly LABS and PLAQUE software to become available to customers during November 2026. The update is separate from the forthcoming PARAMOUNT results, and the precise software versions used throughout the trial should be considered when interpreting the findings.
For clinicians and procurement teams, the distinction between regulatory clearance, analytical validation and prospective clinical utility is essential. A cleared diagnostic tool may provide useful information without every proposed downstream management strategy having been independently validated.
PARAMOUNT could provide additional evidence concerning that clinical utility, but the FDA clearances themselves should not be presented as confirmation of the trial’s outcome.
Could AI-QCT-guided care reduce unnecessary invasive coronary procedures?
One potential benefit of a more detailed noninvasive coronary assessment is improved selection of patients who require further investigation or intervention.
Invasive coronary angiography remains an important diagnostic procedure, particularly when significant coronary disease is suspected or additional information is needed for treatment planning. However, procedures involve costs, resources and potential complications, making appropriate patient selection an important clinical objective.
An AI-QCT-guided strategy could potentially help clinicians distinguish patients who are more likely to benefit from further invasive evaluation from those who may be managed through medical treatment and follow-up.
PARAMOUNT’s registered objectives include evaluating the efficiency of referral for invasive coronary angiography and appropriate percutaneous coronary intervention.
The outcome will depend on how investigators define and assess appropriate use. A reduction in invasive procedures is not automatically beneficial if it results in clinically necessary investigations being missed or delayed.
Conversely, an increase in invasive referrals would not necessarily indicate poorer performance if the additional procedures identify important disease and lead to appropriate treatment.
The central question is whether management becomes more efficient without compromising clinical care. The trial therefore needs to be interpreted through the relationship between diagnostic information, treatment selection and patient outcomes rather than through procedure counts alone.
Medication management is another relevant component. If quantitative plaque analysis changes how clinicians assess coronary disease, it could influence preventive treatment decisions and the intensity of risk-factor management.
However, improved prescribing or greater medication use should not automatically be treated as evidence of reduced cardiovascular events. Demonstrating such benefits would require appropriate outcome studies with sufficient follow-up.
Why will the trial’s 180-day follow-up limit some cardiovascular conclusions?
PARAMOUNT’s announced assessment period is 180 days, making it particularly relevant to near-term management decisions, diagnostic certainty and changes in treatment.
A six-month interval can capture whether clinicians act differently after receiving AI-QCT information. It can also provide data on medication management, further testing and patient-reported symptoms.
However, preventing myocardial infarction and other major cardiovascular events often requires longer observation, particularly when the intervention involves changes in preventive treatment.
The trial should therefore not be expected to establish definitive long-term reductions in heart attacks or cardiovascular mortality simply because management differs between the study groups.
The distinction between management outcomes and hard clinical events is important when evaluating artificial intelligence in cardiology. A technology may improve diagnostic confidence without demonstrating that patients experience fewer hospitalisations or major adverse events.
The primary-results presentation will need to clarify which outcomes were statistically significant and whether those differences were clinically meaningful.
The hierarchical endpoint analysis may also require careful interpretation. If an overall treatment comparison is favourable, investigators will need to show which individual components contributed most strongly to the result.
A finding driven primarily by diagnostic certainty would have different implications from one demonstrating more appropriate invasive interventions or improved management of important cardiovascular risk factors.
Secondary outcomes concerning chest pain, quality of life and medication adherence may provide additional context. However, such results should be assessed according to whether they were prespecified, adequately powered and interpreted with appropriate statistical controls.
The initial trial findings will therefore need to establish what was actually improved before conclusions can be drawn about broader patient benefit.
How could reimbursement affect hospital adoption of Cleerly’s coronary plaque analysis?
Clinical utility is only one component of successful medical software commercialisation. Hospitals and imaging providers must also determine whether diagnostic analysis can be incorporated into existing workflows and reimbursed under applicable payer policies.
The reimbursement environment for AI-enabled coronary plaque analysis has advanced. A Category I Current Procedural Terminology code for quantitative coronary plaque analysis, 75577, became effective in January 2026.
The code provides a recognised billing framework for a defined service, but its existence does not guarantee payment for every patient or clinical circumstance.
Medicare coverage policies for AI-QCT and coronary plaque analysis establish specific eligibility requirements. These can include patients presenting with acute or stable chest pain, no known coronary artery disease and particular findings on an eligible coronary CT angiography study.
Coverage policies may also exclude uses such as asymptomatic screening or certain high-grade stenosis findings. The relevant criteria depend on the applicable Medicare contractor or private-payer policy.
Cleerly announced in January 2026 that Aetna had introduced coverage for its AI-based cardiovascular imaging analysis, joining other insurers that had established relevant policies. The company said participating payers represented more than 86 million covered lives.
That figure reflects the populations associated with insurers offering coverage under defined conditions. It should not be interpreted as the number of people who have undergone Cleerly analysis or as a guarantee that all members qualify for reimbursement.
For providers, the commercial assessment must include the cost of the software, radiology and cardiology workflow, technical infrastructure, interpretation requirements and any additional clinical value generated.
The PARAMOUNT trial could influence that assessment if it demonstrates that AI-QCT-guided management produces more appropriate decisions. However, the relevance of its results will depend on the outcomes observed and the extent to which participating patients resemble those covered under existing payer policies.
How does PARAMOUNT differ from Cleerly’s larger TRANSFORM cardiovascular outcomes trial?
Cleerly is also conducting TRANSFORM, a separate randomised study evaluating a different clinical question: whether coronary artery disease staging can improve cardiovascular prevention in people who are at increased risk but do not have known symptomatic cardiovascular disease.
TRANSFORM is an event-driven outcomes trial involving a planned enrolment of approximately 7,500 participants. As of the company’s latest update, the study was active across 125 United States sites, with primary results anticipated in 2029.
Unlike PARAMOUNT, which focuses on symptomatic patients with stable chest pain and suspected coronary disease, TRANSFORM investigates a prevention strategy in an asymptomatic higher-risk population.
The study is evaluating whether care informed by Cleerly’s investigational Coronary Artery Disease Staging System can reduce heart attacks and other cardiovascular events compared with conventional prevention based on established risk factors.
This distinction is clinically important because evidence supporting a diagnostic management strategy in symptomatic patients does not automatically establish the value of screening or disease staging in people without symptoms.
The risk-benefit balance can differ substantially between the populations. Asymptomatic individuals may face additional imaging, potential incidental findings and subsequent interventions, requiring evidence that any resulting changes in care provide meaningful benefits.
TRANSFORM is intended to address those longer-term questions. Its larger scale and event-driven design may provide a different level of evidence concerning cardiovascular outcomes from that generated by PARAMOUNT’s shorter management-focused investigation.
The two programmes are therefore complementary but should not be conflated. Positive findings in PARAMOUNT would not establish that the TRANSFORM prevention strategy works, just as the outcome of the larger trial cannot be predicted from the earlier study.
What commercial opportunity does PARAMOUNT create for privately held Cleerly?
Cleerly is privately held and has developed a commercial strategy around AI-enabled coronary CT analysis, clinician-facing software and partnerships with imaging and cardiovascular care providers.
The company has invested in regulatory clearances, data development and reimbursement infrastructure to support its products. Its approach involves making quantitative coronary disease information available as part of clinical assessment rather than introducing a new imaging scanner.
This software-led model can potentially operate across compatible imaging environments, allowing the company to reach healthcare providers without requiring every customer to purchase a proprietary CT system.
However, commercial adoption still depends on imaging quality, integration with clinical workflows, trained users and reimbursement. Even when a diagnostic tool can be deployed across multiple institutions, consistent use requires physicians to understand its outputs and incorporate them into clinical decisions.
PARAMOUNT may strengthen the company’s evidence package if the trial demonstrates favourable management outcomes. Such evidence could support discussions with healthcare systems and payers about the value of quantitative analysis.
Nevertheless, a positive result would not necessarily establish lower treatment costs or improved long-term cardiovascular outcomes. The commercial relevance would depend on precisely which clinical decisions changed and whether those changes offer measurable advantages.
Cleerly has not disclosed current revenue, profitability or a specific financial forecast linked to the forthcoming presentation. Its private ownership also means there is no directly observable public-market share-price response to the November announcement.
The strongest commercial interpretation will therefore depend on clinical evidence rather than speculative valuation assumptions.
What should cardiologists and healthcare executives watch when PARAMOUNT results are presented on 7 November?
Todd C. Villines of the University of Virginia is scheduled to present the primary PARAMOUNT results at 9:45 a.m. Central Time on Saturday, 7 November 2026, during the American Heart Association Scientific Sessions in Chicago.
The presentation is expected to provide the first detailed evidence concerning whether Cleerly’s plaque-based management strategy outperformed usual care in the announced 530-patient randomised population.
The most important findings will be the prespecified primary endpoint result, the magnitude of any difference between groups and the statistical strength of the analysis. Investigators will also need to explain the contributions of individual endpoint components and any limitations associated with the study’s open-label design.
Secondary outcomes may clarify whether changes in diagnostic and treatment decisions were accompanied by improvements in chest pain, medication adherence or quality of life. However, the strength of those conclusions will depend on the study’s predefined analytical framework and the completeness of the data.
The presentation should also provide clarity on the relationship between the announced analysis population and the broader registered trial design. This will help readers distinguish the reported results from the full study framework and assess the maturity of the evidence.
For healthcare executives, the findings may influence decisions about adopting AI-enabled plaque analysis within existing coronary CT services. The most persuasive case would connect diagnostic information to appropriate management changes while remaining consistent with reimbursement requirements and clinical practice.
Cleerly’s FDA clearances and improving reimbursement framework provide an established route towards commercial use. PARAMOUNT is intended to test whether that technology can influence care in a controlled comparison against usual management.
The results may represent an important addition to the evidence supporting AI-QCT in symptomatic coronary artery disease, but no efficacy or management advantage can be claimed before the data are presented. For Cleerly, the decisive question on 7 November is whether quantitative coronary plaque and ischaemia information produces better clinical decisions, not merely more detailed diagnostic reports.
