Opportunistic screening took a big step forward this week with new research in Nature Scientific Reports showing how an AI algorithm from Riverain Technologies was able to calculate coronary artery calcium scores from non-contrast CT scans – with performance close to that of radiologists.
Opportunistic screening gives radiologists the chance to detect clinical conditions other than those for which the original scan was ordered.
Potential use cases include calculating cardiovascular risk from mammograms or undiagnosed osteoporosis from CT exams.
One of the opportunistic applications with the most potential is CAC scoring from CT scans.
CAC scores are a good marker for future cardiovascular risk. But it can be time-consuming to perform separate cardiac CT scans just to acquire CAC data when thousands of abdominal and thoracic CT studies are conducted every day and could serve just as well.
Riverain’s ClearRead CT CAC algorithm uses AI to analyze non-contrast CT exams and produce Agatston scores, the reference standard for CAC analysis.
Previous research found Agatston scores to be predictive for both cardiovascular and all-cause mortality, but generating the scores requires some manual involvement from clinicians.
In the new study, Mass General Brigham researchers compared ClearRead CT CAC’s performance to ground-truth calculations from radiologists in 491 patients who got non-contrast CT scans at five U.S. hospitals in 2022 and 2023. Researchers found…
CAC score agreement between AI and radiologists was high, with a kappa of 0.959 (1.0 is perfect agreement).
The association remained strong regardless of sex, age, race, ethnicity, and CT scanner model, with kappa higher than 0.90 for all groups except “other race.”
The AI model’s CAC scores from non-gated CT scans were similar to those from gated cardiac CT exams (kappa = 0.906), which are generally considered the gold standard for cardiac CT but are more complex to perform.
The model’s kappa for gated CT exams compared favorably to recent research conducted with other commercially available algorithms.
The results are a boost for opportunistic screening but in particular for Riverain, which got FDA clearance for ClearRead CT CAC in December 2024 and offers the solution as part of its ClearRead CT suite.
The Takeaway
The new results show that opportunistic screening is moving beyond the research phase and that the opportunity could be now for real-world clinical use.
The 2025 American Heart Association annual conference wraps up today, and cardiac imaging has been a major focus in New Orleans. In particular, research has highlighted imaging’s power to predict future cardiac events – and guide treatment to prevent them.
Coronary artery calcium scoring with CT is a great example, as CAC scores can predict not only cardiovascular but also all-cause mortality.
Another common theme at AHA 2025 has been opportunistic screening, in which data from imaging exams acquired for other clinical indications can be used to detect osteoporosis, cardiovascular disease, and other issues.
Check out the items below for some of the hottest imaging topics at AHA 2025, and for a deeper dive into non-imaging news from New Orleans, be sure to visit our Cardiac Wire sister site.
News from the show’s first three days include…
A massive study of 40k people found that those with CT-derived CAC scores greater than 0 were 2X-3X more likely to die from any cause than people without any CAC – and more died of causes other than cardiovascular disease. Also, 8.5% of patients had other significant findings.
Community health personnel on a Native American reservation were trained to perform point-of-care screening echocardiography assisted by Us2.ai’s AI algorithms.
Us2.ai’s algorithm was also used with transthoracic echo in the SCAN-MP study to detect transthyretin amyloid cardiomyopathy, a cause of heart failure.
Treadmill stress tests fell short compared to CCTA in screening older master’s athletes for ischemia that could lead to sudden cardiac death.
A program in Brazil that used echocardiography to screen schoolchildren for latent rheumatic heart disease led to lower prevalence rates after 10 years (2.5% vs. 4.5%).
Patients with hypertrophic cardiomyopathy who had higher levels of myocardial fibrosis on cardiac MRI were almost 6X more likely to have adverse events over eight years.
HeartLung Technologies’ AI tool predicted CAC presence on CT scans in 2.1k participants in the MESA study with higher AUC than other tools (AUC = 0.73 vs. 0.68).
Another study used HeartLung’s AI to analyze CAC scans to detect myosteatosis – a sign of systemic metabolic dysfunction – which predicted atrial fibrillation and heart failure.
A program promoting CAC scoring to an urban population brought in people for screening who might have been missed through physician referral.
The Takeaway
This week’s news from AHA 2025 shows medical imaging’s contribution to early detection of cardiovascular disease – the leading cause of death worldwide. CT-based CAC scoring has especially promising potential, not only for heart disease but also other conditions through opportunistic screening.
Coronary CT angiography works just as well as traditional stress testing over the long haul for patients with stable symptoms of coronary artery disease. That’s according to the latest follow-up data from the PROMISE study in JAMA Cardiology, which found no difference in mortality between either strategy.
PROMISE was a randomized controlled trial that compared patient work-up with anatomical CCTA scans to functional stress testing (exercise ECG, stress echo, or stress nuclear) in 10k patients from 2010 to 2014.
The first PROMISE results found that in patients with CAD symptoms who were followed up for just over two years, there was little difference between anatomical CCTA and functional stress testing for endpoints like death, myocardial infarction, or other complications.
But what about over a longer follow-up period? The new results extend PROMISE’s follow-up to a median of 10.6 years, finding…
Mortality rates were largely the same whether patients got CCTA or stress testing (14.3% vs. 14.5%, p = 0.56).
Cardiovascular mortality rates were also similar (4.0% vs. 4.3%, p = 0.77).
As were noncardiovascular death rates (10.7% for both).
There were some differences in the predictive power of each modality based on patient characteristics…
With CCTA, any abnormal finding increased a patient’s mortality risk compared to normal findings for severe, moderate, and mild disease (HR = 3.44, 3.38, and 1.99, respectively).
With stress testing, only patients with severely abnormal disease had higher mortality risk (HR = 1.45).
The new PROMISE data also tracks well with recent 10-year findings from SCOT-HEART, another major study that demonstrated CCTA’s value.
Combining results from PROMISE and SCOT-HEART shows 89% survival of patients with stable angina at 12 years, demonstrating good effectiveness regardless of workup strategy.
The Takeaway
PROMISE findings have gone a long way toward showing that CCTA is every bit as effective as stress testing, and the new results reinforce this message. The findings are also good news for radiology, which has a stronger hold over anatomical imaging with CT than it does over the predominant stress modalities, which are largely controlled by cardiology.
People who got evidence of their cardiovascular health from coronary CT angiography scans led healthier lifestyles compared to those who got conventional cardiac risk scoring. That’s according to a new study in JAMA Cardiology that has intriguing ramifications not only for managing heart disease but also for the imaging-based wellness industry.
Cardiovascular disease is the number one killer globally, accounting for one in seven deaths.
The risk of heart disease can be managed through lifestyle changes like better diet and exercise, but getting patients to follow their doctors’ advice can be a challenge.
So researchers in the new study investigated whether data from a patient’s own coronary CT angiography exam would be a better motivational tool compared to simply calculating a risk score based on demographic factors like weight, BMI, and daily step count.
They drew 400 participants from the SCOT-HEART 2 study of CT-based cardiovascular screening in Scotland.
Cardiovascular risk scores were calculated for one group using the ASSIGN criteria for 10-year cardiac event risk, while another group got CCTA scans and were shown their results.
Interventions were recommended for patients in either group based on cardiac risk as calculated by either ASSIGN criteria or CCTA scans, ranging from no interventions to low-level statin therapy to high-intensity statin and enzyme inhibitor treatment.
At six months of follow up, researchers calculated how many participants met the U.K.’s NICE recommendations for diet, body mass index, smoking, and physical exercise, finding …
Nearly three times more CCTA patients complied with NICE healthy lifestyle guidelines (17% vs. 6%).
Fewer CCTA patients were told to start preventive therapy due to their risk (51% vs. 75%).
And of these, CCTA patients were more likely to have followed advice to begin a therapeutic program (77% vs. 46%).
There was no difference in behavior between CCTA patients who saw their own images and those who were told verbally of their results.
In one important fact, the researchers noted that the study was only designed to measure compliance.
It did not assess any change in coronary events over time – these will be addressed in the larger SCOT-HEART 2 study.
The Takeaway
The new study offers powerful evidence that getting their own medical imaging results can drive patients to adopt lifestyle changes that lower their disease risk. In addition to informing cardiovascular disease management, it’s also possible to see these findings employed as part of the wellness screening programs that are becoming increasingly prevalent.
Most of the recent research on calcium scoring has focused on calcium in the coronary arteries and its link to cardiovascular disease. But a new study in American Heart Journal used abdominal CT scans with AI analysis for opportunistic measurement of abdominal aortic calcium to predict cardiac events – possibly earlier than CAC scores.
CT-derived CAC scores have become a powerful tool for predicting cardiovascular disease, helping physicians determine when to begin preventive therapy with treatments like statins.
CAC scores can be generated from dedicated cardiac CT scans, or even lung screening exams as part of a two-for-one test.
Abdominal CT represents another promising area for calculating calcium.
Previous research has found that atherosclerosis in the abdominal aorta may occur before its development in the coronary arteries, creating the opportunity to detect calcium earlier.
Researchers from NYU Langone did just that in the new study, performing abdominal and cardiac CT scans in 3.6k patients and using an AI algorithm they developed in partnership with Visage Imaging to calculate AAC. They found that over an average three-year follow-up period …
AI analysis of AAC severity was positively associated with CAC.
AAC could be used to rule out the presence of CAC relative to two versions of the PREVENT score (AUC=0.701 and 0.7802).
The presence of AAC was associated with a higher adjusted risk of major adverse cardiovascular events (HR=2.18).
A doubling of the AAC score was linked to 11% higher risk of MACE.
The Takeaway
The new results are an exciting demonstration of opportunistic screening’s value, especially given the volume of abdominal CT scans performed annually. AI analysis of routinely acquired abdominal CT could give radiologists a tool for detecting heart disease risk even earlier than what’s possible with CAC scoring.
The American College of Cardiology’s annual meeting is wrapping up today in Chicago, and new research into coronary artery calcium scoring has been one of cardiac imaging’s top trends at McCormick Place.
CAC scoring has been around for ages as a way to detect and quantify calcium buildup in the coronary arteries based on data from non-contrast CT scans.
But it’s only been in recent years that CAC scoring has come into its own as a tool for predicting risk of mortality and major cardiac events – in some cases years before they happen.
Clinicians are learning that they can use CT-generated CAC scores to estimate future risk and guide interventions to reduce it, such by prescribing statins or behavior modifications.
In the CLARIFY CAC screening program, researchers found a 6.2% rate of thoracic aneurysm, indicating a need for screening and prevention.
CAC scores of 0 were more common in women than men (49% vs. 23%), but there was no statistically significant difference in non-calcified plaque rates between genders.
Researchers found moderate accuracy (AUC range=0.60-0.73) for a method of generating CAC scores from 12-lead ECG data rather than non-contrast CT scans.
Bunkerhill Health’s I-CAC algorithm was used to generate automated CAC scores for 200 patients. After six months, patients with scores >400 had a 17% rate of cardiac events and 11% all-cause mortality.
A commonly used measure for low-value care based on administrative claims classified too many CAC tests as inappropriate, with a positive predictive value of only 43%.
A case study focused on the paradox of a 59-year-old healthy triathlete with a CAC score of 780, possibly due to chronic coronary stress from high-endurance exercise. Invasive testing was deferred in favor of medical therapy due to his low cardiac risk.
On the other hand, a literature review of 19.4k people found no statistically significant difference in CAC scores between endurance athletes and healthy controls.
Non-calcified plaque in patients with CAC scores of 0 was common (26%) in residents of rural Appalachia, indicating high risk of rupture and suggesting the limitation of relying on CAC scores.
A Sunday debate discussed whether CAC scoring should be added to mammography and colon cancer screening, or reserved as a decision aid.
The Takeaway
The studies from ACC 2025 show that CAC scoring has a bright future – bright enough that it’s generating heightened interest from cardiology. New CAC scoring tools arriving on the market should improve its predictive value even more.
Cardiac MRI is one of the most powerful imaging tools for assessing heart function, but it’s difficult and time-consuming to perform. Could automated AI planning offer a solution? A new research paper shows how AI-based software can speed up cardiac MRI workflow.
Cardiac MRI has a variety of useful clinical applications, generating high-resolution images for tissue characterization and functional assessment without the ionizing radiation of angiography or CT.
But cardiac MR also requires highly trained MR technologists to perform complex tasks like finding reference cardiac planes, adjusting parameters for every sequence, and interacting with patients – all challenges in today’s era of workforce shortages.
Cardiac MRI’s complexity also increases the number of clicks required by technologists to plan exams.
This can introduce scan errors and produces inter-operator variability between exams.
Fortunately, vendors are developing AI-based software that automates cardiac MR planning – in this case, Siemens Healthineers’ myExam Cardiac Assist and AI Cardiac Scan Companion.
The solution enables single-click cardiac MR planning with a pre-defined protocol that includes auto-positioning to identify the center of the heart and shift the scanner table to isocenter, as well as positioning localizers to perform auto-align without manual intervention.
How well does it work in the real world? Researchers tested the AI software against conventional manual cardiac MR exam planning in 82 patients from August 2023 to February 2024, finding that automated protocols had …
A lower mean rate of procedure errors (0.45 vs. 1.13).
A higher rate of error-free exams (71% vs. 45%).
Shorter duration of free-breathing studies (30 vs. 37 minutes).
But similar duration of breath-hold exams (42 vs. 44 minutes, p=0.42).
While reducing the error gap between more and less experienced technologists.
In their discussion of the study’s significance, the researchers note that most of the recent literature on AI in medical imaging has focused on its use for image reconstruction, analysis, and reporting.
Meanwhile, there’s been relatively little attention paid to one of radiology’s biggest pain points – exam preparation and planning.
The Takeaway
The new study’s results are exciting in that they offer not only a method for performing cardiac MR more easily (potentially expanding patient access), but also address the persistent shortage of technologists. What’s not to like?
Performing automated CT-derived fractional flow reserve with Shukun Technology’s software reduced referrals to invasive coronary angiography by 19% in a new study in Radiology. The findings suggest that software-based FFR-CT can serve a gatekeeper role in managing workup of patients with suspected coronary artery disease.
Cardiac CT has been a revolutionary tool for assessing people with heart problems, evolving rapidly into a first-line modality that’s eclipsed other more traditional imaging technologies.
But CCTA’s prowess also has a downside – more referrals to invasive coronary angiography, in some cases for patients without obstructive disease.
Rising to this challenge is FFR-CT, which uses automated software to calculate maximum blood flow in the coronary arteries and detect dangerous coronary lesions that could be early signs of a cardiac event.
The segment to date has been dominated by Heartflow, thanks to its early start in the field: its FFRCT software got FDA clearance in 2014 and the company has used its dominance to build a massive cash position.
In the new China CT-FFR Study 3, researchers in China used another FFR-CT application, Shukun’s skCT-FFR, and compared angio referral rates for 5.3k patients with suspected coronary artery disease who were scanned with either CCTA alone or CCTA and FFR-CT. They found …
Referral rates were lower for those who got FFR-CT (10% vs. 12.4%), a 19% relative difference.
Fewer cardiac events occurred in the FFR-CT group at one year (0.5% vs. 1.1%).
There was no statistically significant difference in major adverse cardiac event rates at 90 days (0.5% vs. 0.8%, p=0.12) and one year (2.9% vs. 2.8%, p=0.9).
Shukun is not as well known in the West as other developers of FFR-CT software like Heartflow, but the company has raised over $250M to date – enough to land it in the top echelon of AI developers.
One advantage of Shukun that was evident with the new study is that image processing was performed on-site, rather than being shipped off-site as is the case with other applications.
The Takeaway
The study shows that FFR-CT can make cardiac CT more precise while tamping down on referrals to invasive angiography that have come from growing CT use. The results should also help put Shukun on the radar of many industry observers in a segment that so far has been dominated by HeartFlow.
The European Society of Cardiology annual meeting concluded on September 2 in London, with around 32k clinicians from 171 countries attending some 4.4k presentations. Organizers reported that attendance finally rebounded to pre-COVID numbers.
While much of ESC 2024 focused on treatments for cardiovascular disease, diagnosis with medical imaging still played a prominent role.
Cardiac CT dominated many ESC sessions, and AI showed it is nearly as hot in cardiology as it is in radiology.
AI of low-dose CT scans had an AUC of 0.95 for predicting pulmonary congestion, a sign of acute heart failure.
Echocardiography AI identified HFpEF with higher AUC than clinical models (0.75 vs. 0.69).
AI of transthoracic echo detected hypertrophic cardiomyopathy with AUC=0.85.
Another ESC hot topic was CT for calculating coronary artery calcium (CAC) scores, a possible predictor of heart disease. Sessions found …
AI-generated volumetry of cardiac chambers based on CAC scans better predicted cardiovascular events than Agatston scores over 15 years of follow-up in an analysis of 5.8k patients from the MESA study.
AI-CAC with CT was comparable to cardiac MRI read by humans for predicting atrial fibrillation (0.802 vs. 0.798) and stroke (0.762 vs. 0.751) over 15 years, which could give an edge to AI-CAC given its automated nature.
An AI algorithm enabled opportunistic screening of CAC quantification from non-gated chest CT scans of 631 patients, finding high CAC scores in 13%. Many got statins, while 22 got additional imaging and 2 intervention.
AI-generated CAC scores were also highlighted in a Polish study, detecting CAC on contrast CT at a rate comparable to humans on non-contrast CT (77% vs. 79%), possibly eliminating the need for additional non-contrast CT.
The Takeaway
This week’s ESC 2024 sessions demonstrate the vital role of imaging in diagnosing and treating cardiovascular disease. While radiologists may not control the patients, they can always apply knowledge of advances in other disciplines to their work.
Echocardiography is a pillar of cardiac imaging, but it is operator-dependent and time-consuming to perform. In this interview, The Imaging Wire spoke with Seth Koeppel, Head of Business Development, and José Rivero, MD, RCS, of echo AI developer Us2.ai about how the company’s new V2 software moves the field toward fully automated echocardiography.
The Imaging Wire: Can you give a little bit of background about Us2.ai and its solutions for automated echocardiography?
Seth Koeppel: Us2.ai is a company that originated in Singapore. The first version of the software (Us2.V1) received its FDA clearance a little over two years ago for an AI algorithm that automates the analysis and reporting on echocardiograms of 23 key measurements for the evaluation of diastolic and systolic function.
In April 2024 we received an expanded regulatory clearance for more measurements – now a total of 45 measurements are cleared. When including derived measurements, based on those core 45 measurements, now up to almost 60 measurements are fully validated and automated, and with that Us2.V2 is bordering on full automation for echocardiography.
The application is vendor-agnostic – we basically can ingest any DICOM image and in two to three minutes produce a full report and analysis.
The software replicates what the expert human does during the traditional 45-60 minutes of image acquisition and annotation in echocardiography. Typically, echocardiography involves acquiring images and video at 40 to 60 frames per second, resulting in some cases up to 100 individual images from a two- or three-second loop.
The human expert then scrolls through these images to identify the best end-diastolic and end-systolic frames, manually annotating and measuring them, which is time-consuming and requires hundreds of mouse clicks. This process is very operator-dependent and manual.
And so the advantage the AI has is that it will do all of that in a fraction of the time, it will annotate every image of every frame, producing more data, and it does it with zero variability.
The Imaging Wire: AI is being developed for a lot of different medical imaging applications, but it seems like it’s particularly important for echocardiography. Why would you say that is?
José Rivero: It’s well known that healthcare institutions and providers are dealing with a larger number of patients and more complex cases. Echo is basically a pillar of cardiac imaging and really touches every patient throughout the path of care. We bring efficiency to the workflow and clinical support for diagnosis and treatment and follow-ups, directly contributing to enhanced patient care.
Additionally, the variability is a huge challenge in echo, as it is operator-dependent. Much of what we see in echo is subjective, certain patient populations require follow-up imaging, and for such longitudinal follow-up exams you want to remove the inter-operator variability as much as possible.
Seth Koeppel: Echo is ripe for disruption. We are faced with a huge shortage of cardiac sonographers. If you simply go on Indeed.com and you type in “cardiac sonographer,” there’s over 4,000 positions open today in the US. Most of those have somewhere between a $10,000, $15,000, up to $20,000 signing bonus. It is an acute problem.
We’re very quickly approaching a situation where we’re running huge backlogs – months in some situations – to get just a baseline echo. The gold standard for diagnosis is an echocardiogram. And if you can’t perform them, you have patients who are going by the wayside.
In our current system today, the average tech will do about eight echoes a day. An echo takes 45 to 60 minutes, because it’s so manual and it relies on expert humans. For the past 35 years echo has looked the same, there has been no innovation, other than image quality has gotten better, but at same time more parameters were added, resulting in more things to analyze in that same 45 or 60 minutes.
This is the first time that we can think about doing echo in less than 45 to 60 minutes, which is a huge enhancement in throughput because it addresses both that shortage of cardiac sonographers and the increasing demand for echo exams.
It also represents a huge benefit to sonographers, who often suffer repetitive stress injuries due to the poor ergonomics of echo, holding the probe tightly pressed against the patient’s chest in one hand, and the other hand on the cart scrolling/clicking/measuring, etc., which results in a high incidence of repetitive stress injuries to neck, shoulder, wrists, etc.
Studies have shown that 20-30% of techs leave the field due to work-related injury. If the AI can take on the role of making the majority of the measurements, in essence turning the sonographer into more of an “editor” than a “doer,” it has the potential to significantly reduce injury.
Interestingly, we saw many facilities move to “off-cart” measurements during COVID to reduce the time the tech was exposed to the patient, and many realized the benefits and maintained this workflow, which we also see in pediatrics, as kids have a hard time lying on the table for 45 minutes.
So with the introduction of AI in the echo workflow, the technicians acquire the images in 15/20 minutes and, in real-time, the images processed via the AI software are all automatically labeled, annotated, and measured. Within 2-3 minutes, a full report is available for the tech to review, adjust (our measures are fully editable) and confirm, and sign off on the report.
You can immediately see the benefits of reducing the time the tech has the probe in their hand and the patient spends on the table, and the tech then gets to sit at an ergonomically correct workstation (proper keyboard, mouse, large monitors, chair, etc.) and do their reporting versus on-cart, which is where the injuries occur.
It’s a worldwide shortage, it’s not just here in the US, we see this in other parts of the world, waitlist times to get an echo could be eight, 10, 12, or more months, which is just not acceptable.
The OPERA study in the UK demonstrated that the introduction of AI echo can tackle this issue. In Glasgow, the wait time for an echo was reduced from 12 months to under six weeks.
The Imaging Wire: You just received clearance for V2, but your V1 has been in the clinical field for some time already. Can you tell us more about the feedback on the use of V1 by your customers.
José Rivero: Clinically, the focus of V1 was heart failure and pulmonary hypertension. This is a critical step, because with AI, we could rapidly identify patients with heart failure or pulmonary hypertension.
One big step that has been taken by having the AI hand-in-hand with the mobile device is that you are taking echocardiography out of the hospital. So you can just go everywhere with this technology.
We demonstrated the feasibility of new clinical pathways using AI echo out of the hospital, in clinics or primary care settings, including novice screening1, 2 (no previous experience in echocardiography but supported by point-of-care ultrasound including AI guidance and Us2.ai analysis and reporting).
Seth Koeppel: We’re addressing the efficiency problem. Most people are pegging the time savings for the tech on the overall echo somewhere around 15 to 20 minutes, which is significant. In a recent study done in Japan using the Us2.ai software by a cardiologist published in the Journal of Echocardiography, they had a 70% reduction in overall time for analysis and reporting.3
The Imaging Wire: Let’s talk about version 2 of the software. When you started working on V2, what were some of the issues that you wanted to address with that?
Seth Koeppel: Version 1, version 2, it’s never changed for us, it’s about full automation of all echo. We aim to automate all the time-consuming and repetitive tasks the human has to do – image labeling and annotation, the clicks, measurements, and the analysis required.
Our medical affairs team works closely with the AI team and the feedback from our users to set the roadmap for the development of our software, prioritizing developments to meet clinical needs and expectations. In V2, we are now covering valve measurements and further enhancing our performance on HFpEF, as demonstrated now in comparison to the gold standard, pulmonary capillary wedge pressure (PCWP)4.
A new version is really about collaborating with leading institutions and researchers, acquiring excellent datasets for training the models until they reach a level of performance producing robust results we can all be confident in. Beyond the software development and training, we also engage in validation studies to further confirm the scientific efficiency of these models.
With V2 we’re also moving now into introducing different protocols, for example, contrast-enhanced imaging, which in the US is significant. We see in some clinics upwards of 50% to 60% use of contrast-enhanced imaging, where we don’t see that in other parts of the world. Our software is now validated for use with ultrasound-enhancing agents, and the measures correlate well.
Stress echo is another big application in echocardiography. So we’ve added that into the package now, and we’re starting to get into disease detection or disease prediction.
As well as for cardiac amyloidosis (CA), V2 is aligned with guidelines-based measurements for identification of CA in patients, reporting such measurements when found, along with the actual guideline recommendations to support the identification of such conditions which could otherwise be missed
José Rivero: We are at a point where we are now able to really go into more depth into the clinical environment, going into the echo lab itself, to where everything is done and where the higher volumes are. Before we had 23 measurements, now we are up to 45.
And again, that can be even a screening tool. If we start thinking about even subdividing things that we do in echocardiography with AI, again, this is expanding to the mobile environment. So there’s a lot of different disease-based assessments that we do. We are now a more complete AI echocardiography assessment tool.
The Imaging Wire: Clinical guidelines are so important in cardiac imaging and in echocardiography. Us2.ai integrates and refers to guideline recommendations in its reporting. Can you talk about the importance of that, and how you incorporate this in the software?
José Rivero: Clinical guidelines play a crucial role in imaging for supporting standardized, evidence-based practice, as well as minimizing risks and improving quality for the diagnosis and treatment of patients. These are issued by experts, and adherence to guidelines is an important topic for quality of care and GDMT (guideline-directed medical therapies).
We are a scientifically driven company, so we recognize that international guidelines and recommendations are of utmost importance; hence, the guidelines indications are systematically visible and discrepant values found in measurements clearly highlighted.
Seth Koeppel: The beautiful thing about AI in echo is that echo is so structured that it just lends itself so perfectly to AI. If we can automate the measurements, and then we can run them through all the complicated matrices of guidelines, it’s just full automation, right? It’s the ability to produce a full echo report without any human intervention required, and to do it in a fraction of the time with zero variability and in full consideration for international recommendations.
José Rivero: This is another level of support we provide, the sonographer only has to focus on the image acquisition, the cardiologist doing the overreading and checking the data will have these references brought up to his/her attention
With echo you need to include every point in the workflow for the sonographer to really focus on image acquisition and the cardiologist to do the overreading and checking the data. But in the end, those two come together when the cardiologist and the sonographers realize that there’s efficiency on both ends.
The Imaging Wire: V2 has only been out for a short time now but has there been research published on use of V2 in the field and what are clinicians finding?
Seth Koeppel: In V1, our software included a section labeled “investigational,” and some AI measurements were accessible for research purposes only as they had not yet received FDA clearance.
Opening access to these as investigational-research-only has enabled the users to test these out and confirm performance of the AI measurements in independently led publications and abstracts. This is why you are already seeing these studies out … and it is wonderful to see the interest of the users to publish on AI echo, a “trust and verify” approach.
With V2 and the FDA clearance, these measurements, our new features and functionalities, are available for clinical use.
The Imaging Wire: What about the economics of echo AI?
Seth Koeppel: Reimbursement is still front and center in echo and people don’t realize how robust it is, partially due to it being so manual and time consuming. Hospital echo still reimburses nearly $500 under HOPPS (Hospital Outpatient Prospective Payment System). Where compared to a CT today you might get $140 global, MRI $300-$350, an echo still pays $500.
When you think about the dynamic, it still relies on an expert human that makes typically $100,000 plus a year with benefits or more. And it takes 45 to 60 minutes. So the economics are such that the reimbursement is held very high.
But imagine if you can do incrementally two or three more echoes per day with the assistance of AI, you can immediately see the ROI for this. If you can simply do two incremental echoes a day, and there’s 254 days in a working year, that’s an incremental 500 echoes.
If there’s 2,080 hours in a year, and we average about an echo every hour, most places are producing about 2,000 echoes, now you’re taking them to 2,500 or more at $500, that’s an additional $100k per tech. Many hospitals have 8-10 techs scanning in any given day, so it’s a really compelling ROI.
This is an AI that really has both a clinical benefit but also a huge ROI. There’s this whole debate out there about who pays for AI and how does it get paid for? This one’s a no brainer.
The Imaging Wire: If you could step back and take a holistic view of V2, what benefits do you think that your software has for patients as well as hospitals and healthcare systems?
Seth Koeppel: It goes back to just the inefficiencies of echo – you’re taking something that is highly manual, relies on expert humans that are in short supply. It’s as if you’re an expert craftsman, and you’ve been cutting by hand with a hand tool, and then somebody walks in and hands you a power tool. We still need the expert human, who knows where to cut, what to cut, how to cut. But now somebody has given him a tool that allows him to just do this job so much more efficiently, with a higher degree of accuracy.
Let’s take another example. Strain is something that has been particularly difficult for operators because every vendor, every cart manufacturer, has their own proprietary strain. You can’t compare strain results done on a GE cart to a Philips cart to a Siemens cart. It takes time, you have to train the operators, you have human variability in there.
In V2, strain is now included, it’s fully automated, and it’s vendor-neutral. You don’t have to buy expensive upgrades to carts to get access to it. So many, many problems are solved just in that one simple set of parameters.
If we put it all together and look at the potential of AI echo, we can address the backlog, allow for more echo to be done in the echo lab but also in primary care settings and clinics where AI echo opens new pathways for screening and detection of heart failure and heart disease at an early stage, early detection for more efficient treatment.
This helps facilities facing the increasing demand for echo support and creates efficient longitudinal follow-up for oncology patients or populations at risk.
In addition, we can open access to echo exams in parts of the world which do not have the expensive carts nor the expert workforce available and deliver on our mission to democratize echocardiography.
José Rivero: I would say that V2 is a very strong release, which includes contrast, stress echo, and strain. I would love to see all three, including all whatever we had on V1, to be mainstream, and see the customer satisfaction with this because I think that it does bring a big solution to the echo world.
The Imaging Wire: As the year progresses, what else can we look forward to seeing from Us2.ai?
José Rivero: In the clinical area, we will continue our work to expand the range of measurements and validate our detection models, but we are also very keen to start looking into pediatric echo.
Seth Koeppel: Our user interface has been greatly improved in V2 and this is something we really want to keep focus on. We are also working on refining our automated reporting to include customization features, perfecting the report output to further support the clinicians reviewing these, and integrating LLM models to make reporting accessible for non-experts HCP and the patients themselves.
REFERENCES
Tromp, J., Sarra, C., Bouchahda Nidhal, Ben Messaoud Mejdi, Fourat Zouari, Hummel, Y., Khadija Mzoughi, Sondes Kraiem, Wafa Fehri, Habib Gamra, Lam, C. S. P., Alexandre Mebazaa, & Faouzi Addad. (2023). Nurse-led home-based detection of cardiac dysfunction by ultrasound: Results of the CUMIN pilot study. European Heart Journal. Digital Health.
Huang, W., Lee, A., Tromp, J., Loon Yee Teo, Chandramouli, C., Choon Ta Ng, Huang, F., Carolyn S.P. Lam, & See Hooi Ewe. (2023). Point-of-care AI-assisted echocardiography for screening of heart failure (HANES-HF). Journal of the American College of Cardiology, 81(8), 2145–2145.
Hirata, Y., Nomura, Y., Yoshihito Saijo, Sata, M., & Kusunose, K. (2024). Reducing echocardiographic examination time through routine use of fully automated software: a comparative study of measurement and report creation time. Journal of Echocardiography.
Hidenori Yaku, Komtebedde, J., Silvestry, F. E., & Sanjiv Jayendra Shah. (2024). Deep learning-based automated measurements of echocardiographic estimators invasive pulmonary capillary wedge pressure perform equally to core lab measurements: results from REDUCE LAP-HF II. Journal of the American College of Cardiology, 83(13), 316–316.
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