Category: Artificial Intelligence

Guerbet’s Big AI Investment

Posted on by Jake Fishman

Guerbet took a big step towards advancing its AI strategy, acquiring a 39% stake in French imaging software company Intrasense, and revealing ambitious future plans for their combined technologies.

Through Intrasense, Guerbet gains access to a visualization and AI platform and a team of AI integration experts to help bring its algorithms into clinical use. The tie-up could also create future platform and algorithm development opportunities, and the expansion of their technologies across Guerbet’s global installed base.

The €8.8M investment (€0.44/share, a 34% premium) could turn into a €22.5M acquisition, as Guerbet plans to file a voluntary tender offer for all remaining shares.

Even though Guerbet is a €700M company and Intrasense is relatively small (~€3.8M 2022 revenue, 67 employees on LinkedIn), this seems like a significant move given and Guerbet’s increasing emphasis on AI:

What Guerbet was lacking before now (especially since ending its Merative/IBM alliance) was a future AI platform – and Intrasense should help fill that void. 

If Guerbet acquires Intrasense it would continue the recent AI consolidation wave, while adding contrast manufacturers to the growing list of previously-unexpected AI startup acquirers (joining imaging center networks, precision medicine analytics companies, and EHR analytics firms). 

However, contrast manufacturers could play a much larger role in imaging AI going forward, considering the high priority that Bayer is placing on its Calantic AI platform.

The Takeaway

Guerbet has been promoting its AI ambitions for several years, and this week’s Intrasense investment suggests that the French contrast giant is ready to transition from developing algorithms to broadly deploying them. That would take a lot more work, but Guerbet’s scale and imaging expertise makes it worth keeping an eye on if you’re in the AI space.

Federated Learning’s Glioblastoma Milestone

Posted on by Jake Fishman

AI insiders celebrated a massive new study highlighting a federated learning AI model’s ability to delineate glioblastoma brain tumors with high accuracy and generalizability, while demonstrating FL’s potential value for rare diseases and underrepresented populations.

The UPenn-led research team went big, as the study’s 71 sites in 6 continents made it the largest FL project to-date, its 6,314 patients’ mpMRIs created the biggest glioblastoma (GBM) dataset ever, and its nearly 280 authors were the most we’ve seen in a published study. 

The researchers tested their final GBM FL consensus model twice – first using 20% of the “local” mpMRIs from each site that weren’t used in FL training, and second using 590 “out-of-sample” exams from 6 sites that didn’t participate in FL development.

These FL models achieved significant improvements compared to an AI model trained with public data for delineating the three main GBM tumor sub-compartments that are most relevant for treatment planning.

  • Surgically targetable tumor core: +33% w/ local, +27% w/ out-of-sample
  • Enhancing tumor: +27% w/ local, +15% w/ out-of-sample
  • Whole tumor: +16% w/ local, +16% w/ out-of-sample data

The Takeaway

Federated learning’s ability to improve AI’s performance in new settings/populations while maintaining patient data privacy has become well established in the last few years. However, this study takes FL’s resume to the next level given its unprecedented scope and the significant complexity associated with mpMRI glioblastoma exams, suggesting that FL will bring a “paradigm shift for multi-site collaborations.”

The Mammography AI Generalizability Gap

Posted on by Jake Fishman

The “radiologists with AI beat radiologists without AI” trend might have achieved mainstream status in Spring 2020, when the DM DREAM Challenge developed an ensemble of mammography AI solutions that allowed radiologists to outperform rads who weren’t using AI.

The DM DREAM Challenge had plenty of credibility. It was produced by a team of respected experts, combined eight top-performing AI models, and used massive training and validation datasets (144k & 166k exams) from geographically distant regions (Washington state, USA & Stockholm, Sweden).

However, a new external validation study highlighted one problem that many weren’t thinking about back then. Ethnic diversity can have a major impact on AI performance, and the majority of women in the two datasets were White.

The new study used an ensemble of 11 mammography AI models from the DREAM study (the Challenge Ensemble Model; CEM) to analyze 37k mammography exams from UCLA’s diverse screening program, finding that:

  • The CEM model’s UCLA performance declined from the previous Washington and Sweden validations (AUROCs: 0.85 vs. 0.90 & 0.92)
  • The CEM model improved when combined with UCLA radiologist assessments, but still fell short of the Sweden AI+rads validation (AUROCs: 0.935 vs. 0.942)
  • The CEM + radiologists model also achieved slightly lower sensitivity (0.813 vs. 0.826) and specificity (0.925 vs. 0.930) than UCLA rads without AI 
  • The CEM + radiologists method performed particularly poorly with Hispanic women and women with a history of breast cancer

The Takeaway

Although generalization challenges and the importance of data diversity are everyday AI topics in late 2022, this follow-up study highlights how big of a challenge they can be (regardless of training size, ensemble approach, or validation track record), and underscores the need for local validation and fine-tuning before clinical adoption. 

It also underscores how much we’ve learned in the last three years, as neither the 2020 DREAM study’s limitations statement nor critical follow-up editorials mentioned data diversity among the study’s potential challenges.

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.

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.

Imaging AI Funding Still Solid in 2022

Posted on by Jake Fishman

Despite plenty of challenges, imaging AI startups appear to be on pace for another solid funding year, helped by a handful of huge raises and a diverse mix of early-to-mid stage rounds.

So far in 2022 we’ve covered 18 AI funding events that totaled $615M, putting imaging AI startups roughly on pace for 2021’s record-high funding levels ($815M based on Signify’s analysis). Those funding rounds revealed a number of interesting trends:

  • The Big Getting Bigger – $442M of this year’s funding (72% of total) came from just four later-stage rounds: Aidoc ($110M), Viz.ai ($100M), Cleerly ($192M), and Qure.ai ($40M), as VCs increasingly bet on AI’s biggest players. 
  • Rounding Up the Rest – The remaining 14 companies raised a combined $173M (28% of total), with an even mix of Seed/Pre-Seed (4 rounds, $10.5M), Series A (5, $74M), and Series B (5, $89M) rounds. 
  • VCs Heart Cardiovascular AI – Cardiovascular AI startups captured a disproportionate share of VC funding, as Cleerly ($192M) was joined by Elucid ($27M) and Us2.ai ($15M). Considering that Circle CVI was recently acquired for $213M and HeartFlow has raised over $577M, cardiac AI startups seem to have become imaging AI’s valuation leaders (at least alongside diversified and care coordination AI vendors).
  • No H2 Drop-Off (yet) – The funding breakdown between Q1 (6 rounds, $63.5M), Q2 (7, $289M), and Q3 (5, $263M) doesn’t suggest that we’re in the middle of a second-half slowdown… even though we probably are. 

The Takeaway

Despite widespread AI consolidation chatter in Q1 and the emergence of economic headwinds by Q2, imaging AI startups are on pace for yet another massive funding year. These numbers don’t reveal how many otherwise-solid AI startups are struggling to secure their next funding round, and they don’t guarantee that funding will also be strong in 2023, but they do suggest that 2022’s AI funding won’t be nearly as bleak as some naysayers warned.

Multimodal NSCLC Treatment Prediction

Posted on by Jake Fishman

Memorial Sloan Kettering researchers showed that data from routine diagnostic workups (imaging, pathology, genomics) could be used to predict how patients with non-small cell lung cancer (NSCLC) will respond to immunotherapy, potentially allowing more precise and effective treatment decisions.

Immunotherapy can significantly improve outcomes for patients with advanced NSCLC, and it has already “rapidly altered” the treatment landscape. 

  • However, only ~25% of advanced NSCLC patients respond to immunotherapy, and current biomarkers used to predict response have proved to be “only modestly helpful.”  

The researchers collected baseline diagnostic data from 247 patients with advanced NSCLC, including CTs, histopathology slides, and genomic sequencing. 

  • They then had domain experts curate and annotate this data, and leveraged a computational workflow to extract patient-level features (e.g. CT radiomics), before using their DyAM model to integrate the data and predict therapy response.

Using diagnostic data from the same 247 patients, the multimodal DyAM system predicted immunotherapy response with an 0.80 AUC. 

  • That’s far higher than the current FDA-cleared predictive biomarkers – tumor mutational burden and PD-L1 immunohistochemistry score (AUCs: 0.61 & 0.73) – and all imaging approaches examined in the study (AUCs: 0.62 to 0.64).

The Takeaway

Although MSK’s multimodal immunotherapy response research is still in its very early stages and would be difficult to clinically implement, this study “represents a proof of principle” that integrating diagnostic data that is already being captured could improve treatment predictions – and treatment outcomes.

This study also adds to the recent momentum we’re seeing with multi-modal diagnostics and treatment guidance, driven by efforts from academia and highly-funded AI startups like SOPHiA GENETICS and Owkin.

CADx’s Lung Nodule Impact

Posted on by Jake Fishman

A new JACR study highlighted Computer-Aided Diagnosis (CADx) AI’s ability to improve lung nodule malignancy risk classifications, while stating a solid case for the technology’s potential clinical role.

The researchers applied RevealDx’s RevealAI-Lung CADx solution to chest CTs from 963 patients with 1,331 nodules (from 2 LC screening datasets, and one incidental nodule dataset), finding that RevealAI-Lung’s malignancy risk scores (mSI) combined with Lung-RADS would significantly improve…

  • Sensitivity versus Lung-RADS-only (3 cohorts: +25%, +68%, +117%)
  • Specificity versus Lung-RADS-only (3 cohorts: +17%, +18%, +33%)

Looking specifically at the study’s NLST cohort (704 nodules), mSI+Lung-RADS would have…

  • Reclassified 94 nodules to “high risk” (formerly false-negatives)
  • Potentially diagnosed 53 patients with malignant nodules at least one year earlier
  • Reclassified 36 benign nodules to “low-risk” (formerly false-positives)

The RevealDx-based malignancy scores also achieved comparable accuracy to existing clinical risk models when used independently (AUCs: 0.89 vs. 0.86 – 0.88).

The Takeaway

These results suggest that a CADx lung nodule solution like RevealAI-Lung could significantly improve lung nodule severity assessments. Considering the clinical importance of early and accurate diagnosis of high-risk nodules and the many negatives associated with improper diagnosis of low-risk nodules (costs, efficiency, procedures, patient burden), that could be a big deal.

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