What’s the best way to provide supplemental imaging when screening women with dense breasts? A new study this week in Radiologyoffers support for a newer method, whole-breast ultrasound tomography.
It’s well-known by now that dense breast tissue presents challenges to traditional X-ray-based mammography.
In fact, mammography screening’s mortality reduction is far lower in women with dense breasts compared to nondense breasts (13% vs. 41%).
A variety of alternative technologies have been developed to provide supplemental imaging for women with dense breasts, from handheld ultrasound to breast MRI to molecular breast imaging.
One supplemental technology is whole-breast tomography, developed by Delphinus Medical Technologies; the firm’s SoftVue 3D system was approved by the FDA in 2021 as an adjunct to full-field digital mammography for screening women with dense breast tissue.
With SoftVue, women lie prone on a table with the breast stabilized in a water-filled chamber that provides coupling of sound energy between the breast and a ring transducer that scans the entire breast in 2-4 minutes.
Unlike handheld ultrasound, the scanner provides volumetric coronal images that provide a better view of the fat-glandular interface, where many cancers are located.
SoftVue’s performance was analyzed by researchers from USC and the University of Chicago in a retrospective study funded by Delphinus.
They performed SoftVue scans along with digital mammography on 140 women with dense breast tissue from 2017 to 2019; 36 of the women were eventually diagnosed with cancer.
In all, 32 readers interpreted the scans, comparing the performance of FFDM with ultrasound tomography to FFDM alone, finding …
Better performance with FFDM + ultrasound tomography (AUC=0.60 vs. 0.54)
An increase in sensitivity in women with mammograms graded as BI-RADS 4 (suspicious), (37% vs. 30%)
No statistically significant difference in sensitivity in BI-RADS 3 cases (probably benign), (40% vs. 33%, p=0.08)
A mean of 3.3 more true-positive and 0.9 false-negative findings per reader with ultrasound tomography, a net gain of 2.4
The Takeaway
The findings indicate that ultrasound tomography could become a new supplementary tool for imaging women with dense breasts. They are also a shot in the arm for Delphinus, which as a smaller vendor has the challenge of competing with large multinational OEMs that also offer technologies for supplemental breast screening.
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.
Using POCUS in the emergency department (ED) to scan patients with suspected deep vein thrombosis (DVT) cut their length of stay in the ED in half.
Reducing hospital length of stay is one of the holy grails of healthcare quality improvement.
It’s not only more expensive to keep patients in the hospital longer, but it can expose them to morbidities like hospital-acquired infections.
Patients admitted with suspected DVT often receive ultrasound scans performed by radiologists or sonographers to determine whether the blood clot is at risk of breaking off – a possibly fatal result.
But this requires a referral to the radiology department. What if emergency physicians performed the scans themselves with POCUS?
From October 2017 to October 2019, patients presenting at the ED received POCUS scans from emergency doctors trained on the devices.
Results were compared to 135 control patients who got usual care and were sent directly to radiology departments for ultrasound.
Researchers found that POCUS reduced ED length of stay from 4.5 hours to 2.3 hours, a drop of 52%.
Researchers described the findings as “convincing,” especially as they occurred at two different facilities. The results also answer a recent study that found POCUS only affected length of stay when performed on the night shift.
The Takeaway Radiology might not be so happy to see patient referrals diverted from their department, but the results are yet another feather in the cap for POCUS, which continues to show that – when in the right hands – it can have a big impact on healthcare quality.
Wearable devices are all the rage in personal fitness – could wearable breast ultrasound be next? MIT researchers have developed a patch-sized wearable breast ultrasound device that’s small enough to be incorporated into a bra for early cancer detection. They described their work in a new paper in Science Advances.
This isn’t the first use of wearable ultrasound. In fact, earlier this year UCSD researchers revealed their work on a wearable cardiac ultrasound device that obtains real-time data on cardiac function.
The MIT team’s concept expands the idea into cancer detection. They took advantage of previous work on conformable piezoelectric ultrasound transducer materials to develop cUSBr-Patch, a one-dimensional phased-array probe integrated into a honeycomb-shaped patch that can be inserted into a soft fabric bra.
The array covers the entire breast surface and can acquire images from multiple angles and views using 64 elements at a 7MHz frequency. The honeycomb design means that the array can be rotated and moved into different imaging positions, and the bra can even be reversed to acquire images from the other breast.
The researchers tested cUSBr-Patch on phantoms and a human subject, and compared it to a conventional ultrasound scanner. They found that cUSBr-Patch:
Had a field of view up to 100mm wide and an imaging depth up to 80mm
Achieved resolution comparable to conventional ultrasound
Detected cysts as small as 30mm in the human volunteer, a 71-year-old woman with a history of breast cysts
The same cysts were detected with the array in different positions, an important capability for long-term monitoring
The MIT researchers believe that wearable breast ultrasound could detect early-stage breast cancer, in cases such as high-risk people in between routine screening mammograms.
The researchers ultimately hope to develop a version of the device that’s about the size of a smartphone (right now the array has to be hooked up to a conventional ultrasound scanner to view images). They also want to investigate the use of AI to analyze images.
The Takeaway
It’s still early days for wearable breast ultrasound, but the new results are an exciting development that hints of future advances to come. Wearable breast ultrasound could even have an advantage over other wearable use cases like cardiac monitoring, as it doesn’t require continuous imaging during the user’s activities. Stay tuned.
When should breast ultrasound be used as part of mammography screening? It’s often used in cases of dense breast tissue, but other factors should also come into play, say researchers in a new study in Cancer.
Conventional X-ray mammography has difficulties when used for screening women with dense breast tissue, so supplemental modalities like ultrasound and MRI are called into play. But focusing too much on breast density alone could mean that many women who are at high risk of breast cancer don’t get the additional imaging they need.
To study this issue, researchers analyzed the risk of mammography screening failures (defined as interval invasive cancer or advanced cancer) in ~825k screening mammograms in ~377k women, and more than ~38k screening ultrasound studies in ~29k women. All exams were acquired from 2014 to 2020 at 32 healthcare facilities across the US.
Researchers then compared the mammography failure rate in women who got ultrasound and mammography to those who got mammography alone. Their findings included:
Ultrasound was appropriately targeted at women with heterogeneously or extremely dense breasts, with 95.3% getting scans
However, based on their complete risk factor profile, women with dense breasts who got ultrasound had only a modestly higher risk of interval breast cancer compared to women who only got mammography (23.7% vs. 18.5%)
More than half of women undergoing ultrasound screening had low or average risk of an interval breast cancer based on their risk factor profile, despite having dense breasts
The risk of advanced cancer was very close between the two groups (32.0% vs. 30.5%), suggesting that a large fraction of women at risk of advanced cancer are getting only mammography screening with no supplemental imaging
The Takeaway
On the positive side, ultrasound is being widely used in women with dense breast tissue, indicating success in identifying these women and getting them the supplemental imaging they need. But the high rate of advanced cancer in women who only received mammography indicates that consideration of other risk factors – such as family history of breast cancer and body mass index – is necessary beyond just breast tissue density to identify women in need of supplemental imaging.
Performing routine third-trimester ultrasound scans on pregnant women could help identify breech pregnancies, giving women the opportunity to consider alternative birth options. UK researchers in PLOS Medicinesaid the impact was found with both conventional and POCUS ultrasound scanners.
While the incidence of breech presentation at full term is only 3-4%, when breech births do occur they can result in higher morbidity and mortality for both babies and mothers.
In the UK, third-trimester ultrasound scans aren’t routinely performed for low-risk women, missing a chance to give them other options like Cesarean birth.
Therefore, researchers investigated the effectiveness and impact of these scans at two hospitals, one that used conventional ultrasound scanners and the other employing POCUS units (GE HealthCare’s Vscan Air).
At the POCUS facility, scans were typically performed by trained midwives. Women were scanned between 2016 to 2021 at both hospitals.
Performing routine ultrasound scans at 36 weeks reduced the incidence of undiagnosed breech presentation by 71%at the hospital using conventional ultrasound and 69% at the POCUS hospital.
The rate of undiagnosed breech presentation dropped from 14.2% to 2.8% with conventional ultrasound and from 16.2% to 3.5% with POCUS.
The scans also had an impact on babies’ health. Infants born at either facility had less likelihood of a lower Apgar score (<7) five minutes after birth, and babies were less likely to be sent to the neonatal care unit.
The researchers believe their findings suggest a revision of the UK’s clinical guidelines, which don’t currently call for routine third-trimester ultrasound scans for low-risk women. With respect to POCUS, they said their research was the first to investigate the technology for diagnosing fetal presentation, and their findings support wider use of POCUS in areas where conventional ultrasound isn’t available.
The Takeaway
What’s really exciting about this study are the findings about POCUS. Maternal-fetal complications are a huge problem in developing countries and places with less access to imaging technology. POCUS scanners could be used by trained personnel like midwives – perhaps with AI assistance – to identify problems before birth.
We hear a lot about AI’s potential to expand ultrasound to far more users and clinical settings, and a new study out of Singapore suggests that ultrasound’s AI-driven expansion might go far beyond what many of us had in mind.
The PANES-HF trial set up a home-based echo heart failure screening program that equipped a team of complete novices (no experience with echo, or in healthcare) with EchoNous’s AI-guided handheld ultrasound system and Us2.ai’s AI-automated echo analysis and reporting solution.
After just two weeks of training, the novices performed at-home echocardiography exams on 100 patients with suspected heart failure, completing the studies in an average of 11.5 minutes per patient.
When compared to the same 100 patients’ NT-proBNP blood test results and reference standard echo exams (expert sonographers, cart-based echo systems, and cardiologist interpretations), the novice echo AI pathway…
Yielded interpretable results in 96 patients
Improved risk prediction accuracy versus NT-proBNP by 30%
Detected abnormal LVEF <50% scans with an 0.880 AUC (vs. NT-proBNP’s 0.651-0.690 AUCs)
Achieved good agreement with expert clinicians for LVEF<50% detection (k=0.742)
These findings were strong enough for the authors to suggest that emerging ultrasound and AI technologies will enable healthcare organizations to create completely new heart failure pathways. That might start with task-shifting from cardiologists to primary care, but could extend to novice-performed exams and home-based care.
The Takeaway
Considering the rising prevalence of heart failure, the recent advances in HF treatments, and the continued sonographer shortage, there’s clearly a need for more accessible and efficient echo pathways — and this study is arguably the strongest evidence that AI might be at the center of those new pathways.
GE Healthcare’s ultrasound portfolio became a lot more diverse last week with its acquisition of surgical ultrasound company BK Medical. Here’s some details and perspectives:
The Acquisition – GE Healthcare will acquire BK Medical from Altaris Capital Partners for $1.45b, separating BK Medical from Analogic. That’s a pretty big investment considering that GE’s ultrasound unit brings in $3b a year.
GE’s Surgical Expansion – With BK Medical, GE’s ultrasound unit expands from diagnostics to intraoperative imaging and surgical navigation, which is reportedly a fast-growing and high-margin business for BK Medical.
The BK Portfolio – BK Medical got its start in urology ultrasound, and more recently expanded to ultrasound systems used to guide minimally invasive and robotic surgeries and to visualize deep tissue during neuro and abdominal surgeries. That adds up to five unique ultrasound systems.
GE Impact – GE sees a lot of value in BK Medical. BK gives GE an ultrasound portfolio that the other OEMs can’t match (diagnostic, surgical, post-operative), “accelerates” GE’s precision health strategy, and will reportedly deliver “high-single-digit” ROI within five years.
GE Acquisition Trend – While GE Healthcare spent 2018 and 2019 selling major non-imaging businesses (value-based care to Veritas Capital, life sciences to Danaher), GE’s 2020 and 2021 acquisitions have focused on expanding its capabilities within imaging (Zionexa for radiopharmaceuticals, Prismatic Sensors for CT detectors, and now BK Medical for ultrasound). That says a lot about GE Healthcare’s imaging focus, and is quite different from Philips and Siemens, which have increasingly targeted M&A outside of imaging.
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