Month: October 2022

Doubling Down on Software

Posted on by Jake Fishman

A new Bain & Company and KLAS Research report suggests that healthcare providers are “doubling down” on software, while revealing key adoption trends that could have a major impact within radiology.

Software Growth – Despite macroeconomic turbulence, the many provider organizations are actively increasing their software investments.

  • During the last year, 45% of providers accelerated their software investments, while just 10% showed software spending. 
  • Looking towards 2023, over 95% of providers expect to make new software investments and one-third plan to invest more than usual. 
  • Software is now a top five strategic priority for nearly 80% of provider organizations and a top three priority for almost 40%.

Problems & Solutions – The major challenges facing healthcare providers also happen to be the primary software adoption drivers.

  • Nearly 80% of providers who accelerated software investments last year cited labor shortages, wage inflation, or restructuring (M&A, change in leadership) as their top adoption catalysts.
  • Going forward, providers are prioritizing solutions that improve staff productivity and efficiency, capture more patient revenue, and improve security.

From a radiology perspective, that likely means a (continued) focus on solutions that enhance staff productivity and efficiency, improve patient intake/flow, and drive hospital revenue.

Streamlining Tech Stacks – Accelerated software adoption and the proliferation of new software vendors has left providers overwhelmed by their tech stacks.

  • Over 50% of providers are struggling with the flood of offerings, and 24% believe that their existing tech stack keeps them too busy to stay current on new solutions.
  • Providers also cite poor cross-solution interoperability as their top tech stack pain point.
  • As a result, 72% of providers will first attempt to add software from existing vendors before considering new companies, and 63% are looking to reduce the number of third-party solutions in their tech stacks.

In radiology, this trend likely favors the leading informatics players, while fortifying the value propositions of AI platform/marketplace vendors, unified imaging IT vendors, and extremely well-integrated point solutions.

The Takeaway

Even if Bain is targeting a broad healthcare audience, it’s clear that the macro trends highlighted in this report are having a similar impact within radiology, giving imaging teams and their vendors a solid framework to guide the next phases of their software adoption.

CCTA AI Predicts Ischemia and MBF

Posted on by Jake Fishman

A Cedars-Sinai and Amsterdam UMC-led team developed a machine learning system that analyzes quantitative plaque in coronary CTA exams to identify patients with ischemia and impaired myocardial blood flow (MBF), potentially creating an alternative to current methods.

The researchers trained the ML model using invasive FFR data from 254 patients (484 FFR vessels) to predict ischemia and impaired MBF by analyzing plaque data in CCTA exams. 

They then tested it with CCTAs from 208 patients (581 vessels) who also underwent invasive FFR and H2O PET exams, finding that the CCTA ML scores:

  • Predicted FFR-defined ischemia far more accurately than standard CCTA stenosis evaluations, while rivaling FFRCT assessments (AUCs: 0.92 vs. 0.84 & 0.93)
  • Predicted PET-based impaired MBF more accurately than standard CCTA stenosis evaluations and FFRCT assessments (AUCs: 0.80 vs. 0.74 & 0.77)

Because the ML scoring system operates locally, the authors highlighted its potential to quickly assess high-risk patients before invasive coronary angiography (avoiding off-site processing delays) or to assess low-risk patients at earlier stages, helping to improve ICA efficiency and accuracy.

The researchers plan to continue to develop their CCTA plaque AI solution, including adding more plaque features and CCTA metrics, and potentially seeking regulatory approval depending on the results of future validation studies.

The Takeaway

CCTA plaque AI is already one of the hottest segments on the commercial side of imaging AI, and this study highlights similar advances in academic centers, while showing that CCTA plaque AI can quickly and accurately predict both ischemia and lower MBF.

Prenuvo’s Longevity Imaging Funding

Posted on by Jake Fishman

There are few medical imaging segments with more momentum among the general public than longevity-focused MRI screening, and Silicon Valley startup Prenuvo just raised a $70M Series A round to capitalize on that momentum.

Prenuvo’s imaging centers help longevity-minded individuals detect health problems early, combining full-body MRI exams, AI analysis, and radiologist interpretations to screen for over 500 conditions (catching most solid tumors while still Stage 1). 

  • Prenuvo places a high emphasis on the patient experience, including comfort, entertainment, exam speed, and patient-friendly reporting, all reportedly at a “fraction of the cost of traditional MRI screenings.”
  • Already growing at a 240% annual rate, Prenuvo will use its new funding to expand its AI team, accelerate its technology development, build its custom MRI scanners, and grow its presence from 6 to 16 imaging centers nationwide.

Many radiologists have made it clear that they don’t approve of proactive screening, noting the high risk of overdiagnosis and unnecessary radiation exposure (w/ CT screenings) when patients undergo just-in-case scans. 

However, there are enough stories about proactive exams catching early diseases and enough people who want to live as long as possible to inspire a growing field of longevity imaging startups, including BrainKey (brain longevity), Ezra (cancer screening), Q Bio (overall health), and Human Longevity (overall health).

The Takeaway

Whether radiologists like it or not, there are plenty of people who want to maximize their longevity and there’s apparently plenty of venture funding available to longevity imaging startups ($70M is a huge Series A for an imaging startup in 2022). 

We might still be early in the longevity imaging trend, but if it intensifies it would mean a lot more imaging exams, a growing source of early diagnoses (and incidental findings), and potentially greater longevity for the patients who can afford these services.

GE Healthcare Launches All-Digital Omni Legend PET/CT

Posted on by Jake Fishman

GE Healthcare announced a major update to its molecular imaging lineup, launching the all-digital Omni Legend PET/CT.

The FDA-cleared and CE-marked Omni Legend is the first product to launch from GE’s new Omni PET/CT platform, and leverages an array of new technologies that drive big improvements to image quality, workflow efficiency, clinical versatility, and precision medicine. 

  • dBGO Detector – The Omni Legend is highlighted by its new digital BGO detector (dBGO), which provides 2.2-times higher sensitivity, 16% to 20% improved small lesion detection, and 53% faster PET scans.
  • Precision DL – GE’s new Precision DL software expands the Omni Legend’s support for tracers “beyond FDG” (using image processing) and enhances image quality (using deep learning image reconstruction).
  • Clinical Versatility – The above combination of sensitivity, image quality, and tracer compatibility allows the Omni Legend PET/CT to support a wider range of oncology, cardiac, and neuro use cases, in addition to supporting the diagnostics role within theranostics.
  • Efficiency Forward – In addition to faster scan times, the Omni Legend supports GE’s efficiency solutions that streamline calibration (data quality assurance), simplify protocol selection (new UI), and reduce labor for patient positioning (AI-based Auto Positioning Camera).
  • Future Ready – Like GE’s other recent advanced imaging launches, the Omni platform is built to support future upgrades, covering all core dimensions of PET/CT imaging (axial FOV, detector, software, CT, tracers, etc.).

The Takeaway

A completely new PET/CT platform doesn’t come along very often, and GE Healthcare seems to have made the most of this rare occasion with Omni PET/CT, bringing many improvements that imaging teams are seeking today, along with the theranostics support and component upgradability that should pay off in the future.

Annalise.ai Gets ‘Comprehensive’ with Enterprise CTB

Posted on by Jake Fishman

Annalise.ai doubled-down on its comprehensive AI strategy with the launch of its Annalise Enterprise CTB solution, which identifies a whopping 130 different non-contrast brain CT findings. 

Initially available for clinical use in the UK, Australia, and New Zealand, Annalise Enterprise CTB analyzes brain CTs as they are acquired, prioritizes urgent cases, and provides radiologists with details on each finding (types, locations, likelihood).  

If this sounds familiar, it’s because Annalise.ai’s original Enterprise CXR solution identifies 124 different chest X-ray findings, with previous clinical studies showing that it improves radiologists’ detection accuracy, diagnostic decision making, and reporting speed. 

We’re also seeing a (less-extreme) push towards comprehensive AI from other vendors, as Qure.ai’s brain CT solution detects 11 findings and a growing field of chest X-ray AI vendors lead with their ability to detect multiple findings (also Lunit, Qure.ai, Oxipit, Vuno).

The Takeaway

Whether Annalise.ai’s 10x-larger list of findings results in a similar performance advantage will be decided in the clinic, but Annalise Enterprise CTB and CXR (and any future solutions) should go a long way towards supporting radiology teams who want to improve their detection performance without patching together multiple “narrow AI” solutions .

iCAD and Solis CVD Alliance

Posted on by Jake Fishman

iCAD and major breast imaging center company Solis Mammography announced plans to develop and commercialize AI that quantifies breast arterial calcifications (BACs) in mammograms to identify women with high cardiovascular disease (CVD) risks.

Through the multi-year alliance, iCAD and Solis will expand upon iCAD’s flagship ProFound AI solution’s ability to detect and quantify BACs, with the goal of helping radiologists identify women with high CVD risks and guide them into care.

iCAD and Solis’ expansion into cardiovascular disease screening wasn’t exactly expected, but recent trends certainly suggest that commercial AI-based BAC detection could be on the way: 

  • There’s also mounting academic and commercial momentum behind using AI to “opportunistically” screen for incidental findings in scans that were performed for other reasons (e.g. analyzing CTs for CAC scores, osteoporosis, or lung nodules).
  • Despite being the leading cause of death in the US, it appears that we’re a long way from formal heart disease screening programs, making the already-established mammography screening pathway an unlikely alternative.
  • Volpara and Microsoft are also working on a mammography AI product that detects and quantifies BACs. In other words, three of the biggest companies in breast imaging (at least) and one of the biggest tech companies in the world are all currently developing AI-based BAC screening solutions.

The Takeaway

Widespread adoption of mammography AI-based cardiovascular disease screening might seem like a longshot to many readers who often view incidentals as a burden and have grown weary of early-stage AI announcements… and they might be right. That said, there’s plenty of evidence suggesting that a solution like this would help detect more early-stage heart disease using scans that are already being performed.

Google Launches Cloud Medical Imaging Suite

Posted on by Jake Fishman

Google announced what might be its biggest, or at least most public, push into medical imaging AI with the launch of its new Google Cloud Medical Imaging Suite.

The Suite directly targets organizations who are developing imaging AI models and performing advanced image-based analytics, while also looking to improve Google’s positioning in the healthcare cloud race.

The Medical Imaging Suite is (logically) centered around Google Cloud’s image storage and Healthcare API, which combine with its DICOMweb-based data exchange and automated DICOM de-identification tech to create a cloud-based AI development environment. Meanwhile, its “Suite” title is earned through integrations with an array of Google and partner solutions:

  • NVIDIA’s annotation tools (including its MONAI toolkit) to help automate image labeling
  • Google’s BigQuery and Looker solutions to search and analyze imaging data, and create training datasets
  • Google’s Vertex AI environment to accelerate AI pipeline development
  • NetApp’s hybrid cloud services to support on-premise-to-cloud data management
  • Google’s Anthos solution for centralized policy management and enforcement
  • Change Healthcare’s cloud-native enterprise imaging PACS for clinical use

It’s possible that many of these solutions were already available to Google Cloud users, and it appears that AWS and Azure have a similar list of imaging capabilities/partners, so this announcement might prove to be more technologically significant if it leads to Google Cloud creating a differentiated and/or seamlessly-integrated suite going forward.

However, the announcement’s marketing impact was immediate, as press articles and social media conversations largely celebrated Google Cloud’s new role in solving imaging’s interoperability and AI development problems. It’s been a while since we’ve seen AWS or Azure gain imaging headlines or public praise like that, and they’re the healthcare cloud market share leaders.

The Takeaway

Although some might debate whether the Medical Imaging Suite’s features are all that new, last week’s launch certainly reaffirms Google Cloud’s commitment to medical imaging (with an AI development angle), and suggests that we might see more imaging-targeted efforts from them going forward.

Arterys and Tempus’ Precision Merger

Posted on by Jake Fishman

Arterys was just acquired by precision medicine AI powerhouse Tempus Labs, marking perhaps the biggest acquisition in the history of imaging AI, and highlighting the segment’s continued shift beyond traditional radiology use cases. 

Arterys has become one of imaging’s AI platform and cardiac MRI 4D flow leaders, leveraging its 12 years of work and $70M in funding to build out a large team of imaging/AI experts, a solid customer base, and an attractive intellectual property portfolio (AI models, cloud viewer, and a unique multi-vendor platform).

Tempus Labs might not be a household name among Imaging Wire readers, but they’ve become a giant in the precision medicine AI space, using $1.1B in VC funding and the “largest library of clinical & molecular data” to develop a range of precision medicine and treatment discovery / development / personalization capabilities.

It appears that Arterys will continue to operate its core radiology AI business (with far more financial support), while supporting the imaging side of Tempus’s products and strategy.

This acquisition might not be as unprecedented as some think. We’ve seen imaging AI assume a central role within a number of next-generation drug discovery/development companies, including Owkin and nference (who recently acquired imaging AI startup Predible), while imaging AI companies like Quibim are targeting both clinical use and pharma/life sciences applications.

Of course, many will point out how this acquisition continues 2022’s AI shakeup, which brought at least five other AI acquisitions (Aidence & Quantib by RadNet; Nines by Sirona, MedoAI by Exo, Predible by nference) and two strategic pivots (MaxQ AI & Kheiron). Although these acquisitions weren’t positive signs for the AI segment, they revealed that imaging AI startups are attractive to a far more diverse range of companies than many could have imagined back in 2021 (including pharma and life sciences).

The Takeaway

Arterys just transitioned from being an independently-held leader of the (promising but challenged) diagnostic imaging AI segment to being a key part of one of the hottest companies in healthcare AI, all while managing to keep its radiology business intact. That might not be the exit that Arterys’ founders envisioned, but in many ways it’s an ideal second chapter.

Plaque AI’s First Reimbursement

Posted on by Jake Fishman

The small list of cardiac imaging AI solutions to earn Medicare reimbursements just got bigger, following CMS’ move to add an OPPS code for AI-based coronary plaque assessments. That represents a major milestone for Cleerly, who filed for this code and leads the plaque AI segment, and it marks another sign of progress for the business of imaging AI.

With CMS’ October 1st OPPS update, Cleerly and other approved plaque AI solutions now qualify for $900 to $1,000 reimbursements when used with Medicare patients scanned in hospital outpatient settings. 

  • That achievement sets the stage for plaque AI’s next major reimbursement hurdle: gaining coverage from local Medicare Administrative Contractors (MACs) and major commercial payers.

Cleerly and its qualifying plaque AI competitors join a growing list of Medicare-reimbursed imaging AI solutions, headlined by HeartFlow’s FFRCT solution ($930-$950) and Perspectum’s LiverMultiScan MRI software ($850-$1,150), both of which have since expanded their reimbursements across MAC regions and major commercial payers. 

  • The last few years also brought temporary NTAP reimbursements for Viz.ai (LVO detection / coordination), Caption Health (echo AI guidance), and Optellum (lung cancer risk assessments), plus a growing number of imaging AI CPT III codes that might lead to future reimbursements.

The new reimbursement should also drive advancements within the CCTA plaque AI segment, giving providers more incentive to adopt this technology, and providing emerging plaque AI vendors (e.g. Elucid, Artrya) a clearer path towards commercialization and VC funding.

The Takeaway

CMS’ new plaque AI OPPS code marks a major milestone for Cleerly’s commercial and clinical expansion, and a solid step for the plaque AI segment. 

The reimbursement also adds momentum for the overall imaging AI industry, which finally seems to be gaining support from CMS. That’s good news for AI vendors, since it’s pretty much proven that reimbursements drive AI adoption and are often necessary to show ROI.

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