Category: Artificial Intelligence

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.

Viz.ai Adds PE Stratification

Posted on by Jake Fishman

Viz.ai announced the FDA clearance of its new RV/LV ratio algorithm, adding an important risk stratification feature to its pulmonary embolism AI module, while representing an interesting example of how triage AI solutions might evolve.

Triage + Stratification + Coordination – Viz PE becomes far more comprehensive with its new RV/LV integration, helping radiologists detect/prioritize PE cases and assess right heart strain (a major cause of PE mortality), while equipping PE response teams with more actionable information. 

  • This addition might also improve clinicians’ experience with Viz PE, noting the risk of developing AI “alert fatigue” when all severity levels are treated the same.

Viz.ai is So On-Trend – Signify Research recently forecast that AI leaders will increasingly expand into new clinical segments, enhance their current solutions, and leverage platform / marketplace strategies, as AI evolves from point solutions to comprehensive workflows. Those trends are certainly evident within Viz.ai’s recent PE strategy…

  • Viz PE’s late 2021 launch was a key step in Viz.ai’s expansion beyond neuro/stroke
  • Adding RV/LV risk stratification certainly enhances Viz PE’s detection capabilities
  • Viz PE was developed by Avicenna.AI, arguably making Viz.ai a platform vendor
  • Viz PE’s workflow now combines detection, assessment, and care coordination

The same could be said for Aidoc, which previously added Imbio’s RV/LV algorithm to its PE AI solution (and also supports incidental PE), although few other triage AI workflows are this advanced for PE or other clinical areas.

The Takeaway

Viz.ai’s PE and RV/LV integration is a great example of how detection-focused AI tools can evolve through risk/severity stratification and workflow integration — and it might prove to be a key step in Viz.ai’s expansion beyond stroke AI.

Prioritizing Length of Stay

Posted on by Jake Fishman

A new study out of Cedars Sinai provided what might be the strongest evidence yet that imaging AI triage and prioritization tools can shorten inpatient hospitalizations, potentially bolstering AI’s economic and patient care value propositions outside of the radiology department.

The researchers analyzed patient length of stay (LOS) before and after Cedars Sinai adopted Aidoc’s triage AI solutions for intracranial hemorrhage (Nov 2017) and pulmonary embolism (Dec 2018), using 2016-2019 data from all inpatients who received noncontrast head CTs or chest CTAs.

  • ICH Results – Among Cedars Sinai’s 1,718 ICH patients (795 after ICH AI adoption), average LOS dropped by 11.9% from 10.92 to 9.62 days (vs. -5% for other head CT patients).
  • PE Results – Among Cedars Sinai’s 400 patients diagnosed with PE (170 after PE AI adoption), average LOS dropped by a massive 26.3% from 7.91 to 5.83 days (vs. +5.2% for other CCTA patients). 
  • Control Results – Control group patients with hip fractures saw smaller LOS decreases during the respective post-AI periods (-3% & -8.3%), while hospital-wide LOS seemed to trend upward (-2.5% & +10%).

The Takeaway

These results were strong enough for the authors to conclude that Cedars Sinai’s LOS improvements were likely “due to the triage software implementation.” 

Perhaps more importantly, some could also interpret these LOS reductions as evidence that Cedars Sinai’s triage AI adoption also improved its overall patient care and inpatient operating costs, given how these LOS reductions were likely achieved (faster diagnosis & treatment), the typical associations between hospital long stays and negative outcomes, and the fact that inpatient stays have a significant impact on hospital costs.

AI Crosses the Chasm

Posted on by Jake Fishman

Despite plenty of challenges, Signify Research forecasts that the global imaging AI market will nearly quadruple by 2026, as AI “crosses the chasm” towards widespread adoption. Here’s how Signify sees that transition happening:

Market Growth – After generating global revenues of around $375M in 2020 and $400M and 2021, Signify expects the imaging AI market to maintain a massive 27.6% CAGR through 2026 when it reaches nearly $1.4B. 

Product-Led Growth – This growth will be partially driven by the availability of new and more-effective AI products, following:

  • An influx of new regulatory-approved solutions
  • Continued improvements to current products (e.g. adding triage to detection tools)
  • AI leaders expanding into new clinical segments
  • AI’s evolution from point solutions to comprehensive solutions/workflows
  • The continued adoption AI platforms/marketplaces

The Big Four – Imaging AI’s top four clinical segments (breast, cardiology, neurology, pulmonology) represented 87% of the AI market in 2021, and those segments will continue to dominate through 2026. 

VC Support – After investing $3.47B in AI startups between 2015 and 2021, Signify expects that VCs will remain a market growth driver, while their funding continues to shift toward later stage rounds. 

Remaining Barriers – AI still faces plenty of barriers, including limited reimbursements, insufficient economic/ROI evidence, stricter regulatory standards (especially in EU), and uncertain future prioritization from healthcare providers and imaging IT vendors. 

The Takeaway

2022 has been a tumultuous year for AI, bringing a number of notable achievements (increased adoption, improving products, new reimbursements, more clinical evidence, big funding rounds) that sometimes seemed to be overshadowed by AI’s challenges (difficult funding climate, market consolidation, slower adoption than previously hoped).  

However, Signify’s latest research suggests that 2022’s ups-and-downs might prove to be part of AI’s path towards mainstream adoption. And based on the steeper growth Signify forecasts for 2025-2026 (see chart above), the imaging AI market’s growth rate and overall value should become far greater after it finally “crosses the chasm.”

Get every issue of The Imaging Wire, delivered right to your inbox.

You might also like..

Select All

You're signed up!

It's great to have you as a reader. Check your inbox for a welcome email.

-- The Imaging Wire team

You're all set!