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Signify Premium Insight: Risk and Reward – The Maturation of Medical Imaging AI

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Earlier this month, Viz.ai announced that it had received US-FDA clearance for its automated right ventricle/left ventricle (RV/LV) ratio algorithm, a new component of the vendor’s Pulmonary Embolism (PE) solution. The RV/LV algorithm will enable the automated assessment of potential right ventricle dilation and therefore help to identify right ventricular dysfunction, before delivering the results quickly to the entire care team using Viz’s PE solution.

The move represents the latest FDA clearance for Viz.ai, as it continues to grow its care coordination platform and expand beyond its original stroke care remit. The move also highlights a growing trend in medical imaging AI of vendors expanding product portfolios beyond a single use case, and also beyond image analysis.

The Signify View

As medical imaging AI vendors have matured and proved themselves worthy of increasingly lofty funding rounds, companies are having to expand beyond their original briefs to continue to provide value to the doctors that use them. Some of the most successful vendors have sought to offer this increased value by adding additional capabilities along the care pathway, beyond the slice of the workflow devoted to image analysis itself.

In the case of Viz.ai, this originally meant expanding into elements of stroke care such as triage and decision support, with the vendor’s care coordination platform aiming to expedite the treatment of the most urgent cases. Latterly, instead of expanding along the care pathway, vendors have been looking to leverage their expertise more broadly, with Viz, expanding into other vascular conditions.

For Viz, and other vendors, the key to adoption isn’t just about  the detection algorithms themselves. While their effectiveness is important, slight variances in specificity and sensitivity between vendors won’t make or break a provider’s decision to go ahead and make a purchase –  instead the value comes from the care coordination platform and the value that an AI developer can offer across the whole workflow. This is key as they translate their expertise into other areas. There may be niche vendors with slightly more performant algorithms in certain specific tasks, but these vendors will not be able to match the value brought about by a complete care coordination platform.

There are risks to this approach, however. Viz.ai, and other peers adopting a similar strategy such as Aidoc, and some Chinese vendors have been able to raise considerable amounts of money by advancing into new clinical areas and broadening their product portfolio. While such moves give them a head start over some more specialist vendors, they may also risk spreading themselves too thinly, stymieing their ability to fully deliver on their promises in the areas they first gained success.

Better Together?

Some vendors are forging partnerships to mitigate this exposure. Aidoc, for example, has chosen to add quantification capabilities to both its stroke care and pulmonary embolism solutions by looking externally. Aidoc’s own detect and triage capabilities are bolstered by a perfusion solution from I cometrix for stroke, and RV/LV solution from Imbio for its pulmonary embolism solution. This has allowed Aidoc to strengthen its care coordination platform, bringing quantification and stratification tools to market, while its partner gains access to many of Aidoc’s sites, giving the vendor significant potential upsell opportunities.

Unlike Aidoc, Viz developed the entirety of its stroke care platform in-house. However, for its pulmonary embolism solution, it also turned to a partner, forging links with Avicenna.ai to deliver the detect and triage capabilities for pulmonary embolism. While such a move will see the vendor relinquish some control, partnership offers a significantly expedited rollout. Rather than starting from scratch, having to develop a solution and conduct clinical validation studies over multiple years, a timespan that could result in the vendor losing ground to competitors.

Adopting such a strategy also requires Viz to further develop a back-end architecture for the native and partner algorithms to work seamlessly together, a move which could see the vendor follow in the footsteps of Aidoc and herald the commercial launch of an integrated AI platform.

The Importance of Being Useful

Regardless of the specifics surrounding vendors’ expansions into other clinical areas, be it Viz or any other AI vendor, the approach of leveraging triage and stratification tools is significant. For instance, it highlights that instead of being content with offering tools only useful for image analysis in other clinical areas, developing fully fledged care coordination platforms to serve other clinical situations is now a clear priority. Whether the actual image analysis part of that solution is developed internally, or offered via a partnership is fast becoming immaterial, as the real value of such solutions doesn’t stem from image analysis itself. Instead, in many cases, providers will benefit from leading AI vendors’ abilities to bring imaging analysis algorithms into a considered workflow, to increase their utility.

Some tools, also confer other advantages. Triage tools for example, have a simpler regulatory pathway than CADe or CADx image analysis algorithms, which, are seen to harbour more potential for patient harm. This can offer vendors a more efficient route to market. While the products they will be able to sell as a result of the approval may be more limited compared to solutions cleared for diagnostic use, such clearances will at least enable vendors to begin generating revenue and launch commercially in new markets, offering them a foundation to build on.

More broadly the expansion of some of medical imaging AI’s largest vendors into wider clinical areas, seeing them apply their expertise into more diverse use cases represents the growing maturation of medical imaging AI vendors.

Remember the Objectives

The ultimate aim of medical imaging AI is not to shave seconds of the read time of a chest X-ray, for example or even identify the presence of an indicator of a clinical condition. It is, above all else intended to improve patient outcomes; a final result that is based on the totality of a patient’s care, along their entire care journey.

The portion of this journey that actually entails the analysis of medical images is small. As such, although image analysis is the use case for AI that is discussed most excitedly, there are opportunities elsewhere along the care pathway that can have a more substantial impact on patients’ eventual outcomes. The addition of risk stratification tools such as the RV/LV algorithm from Viz epitomises this.

The vendor’s USP has long been to apply its expertise beyond the image analysis portion of the workflow with its care coordination platform. Not only does this deliver the assistance to identify findings from medical images, but it also helps imaging departments, and other departments more broadly, to better manage patient care and make interventions earlier. Compared to the relatively slight impact that shaving a few seconds off a read time can have for a provider, even for high read volume applications, the use of AI in this broader way can be far more significant.

Further, this offers a more sophisticated method of identifying the leaders in the medical imaging AI market compared with simply looking at which vendor has the greatest number of FDA cleared algorithms, or which has been able to raise the most capital. Instead, it is increasingly possible to assess vendors based on how sophisticated their tools are, and how much value they can offer providers. There is no single, solitary route to adding this value, with comprehensive solutions, and some sophisticated point solutions, which alter the diagnostic pathway, also offering broader value to providers alongside some vendors’ expansion into additional clinical areas, and along care pathways (end-to-end solutions as previously termed by Signify Research).

In this regard, broader imaging IT vendors have an advantage. With large installed bases and their presence across radiology departments and beyond, these vendors, with the right tools, could alleviate many of the bottlenecks faced by providers. However, at present these vendors aren’t aggressively leveraging this advantage, leaving the likes of Viz and its peers to make the early headway.

Whether they are able to capitalise long-term remains to be seen, but for now at least, moves such as that made by Viz, and some of its peers, show the maturation of medical imaging AI away from a “one-trick” image analysis focus toward impactful care outcomes.

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Signify Premium Insight: Viz.ai, Hyperfine and Maximising Mobile MRI

This Insight is part of your subscription to Signify Premium Insights – Medical ImagingThis content is only available to individuals with an active account for this paid-for service and is the copyright of Signify Research. Content cannot be shared or distributed to non-subscribers or other third parties without express written consent from Signify ResearchTo view other recent Premium Insights that are part of the service please click here.

Last month a partnership was formed between stroke AI specialist, Viz.ai, and point-of-care MRI start-up, Hyperfine. The move aims to give clinicians access to MRI capability at a patient’s bedside, arming them with more diagnostic information for assessing and managing stroke care. This, the vendors hope, will ultimately allow patients suffering from a suspected stroke, a highly time-sensitive condition, to be treated more quickly.

The partnership will see Viz.ai’s care coordination platform utilised alongside Hyperfine’s Swoop portable MRI system, serving as a new commercial opportunity.

The Signify View

Viz.ai has been among the most successful medical imaging AI vendors over recent years. It was, for instance, among the earliest vendors to secure reimbursement for the use of its solution via the New Technology Add-on Payment (NTAP), it has been highly successful in funding rounds, laying claim to more than $250m, and has enjoyed a healthy succession of regulatory approvals. In meeting these milestones, Viz.ai has now found itself facing an ongoing difficulty; how best to get its solution into hospitals.

To this end, the vendor has adopted several initiatives. As well as making direct sales to providers, it has also looked to enhance its offering by partnering with other vendors. It has, for example, sealed a deal with Avicenna.ai in order to bring better care coordination for patients suffering from pulmonary embolism and aortic disease. Elsewhere, Viz.ai has also partnered with Medtronic in a bid to accelerate the adoption of its solution across Europe.

The AI vendor’s partnership with Hyperfine continues this direction, representing another route to market for Viz.ai, and offering another route by which its care coordination platform can get in the hands of users.

This route will not be, for the time being at least, widely used. Hyperfine is, after all, a young vendor with a very small installed base. It does however offer significant clinical potential, allowing MRI imaging to be conducted bedside, and therefore enabling patients in emergency rooms and intensive care units to quickly be assessed for stroke without needing to be moved.

Further, embedding Viz.ai’s solution on Hyperfine’s hardware also ties into the AI vendor’s broader ambition of effectively ‘solving’ stroke care, and addressing and expediting every facet of diagnosing the illness. Viz already offers a solution that can support clinicians along almost every stage of the stroke care pathway. By embedding the solution on a device that will be used in emergency rooms, Viz is continuing to expound one of its core selling points. Even if it is rarely used, having the option to use Viz.ai’s solution in the emergency room further facilitates this end-to-end vision.

Solving Stroke Care

The agreement also makes sense for Hyperfine. The young vendor has previously highlighted stroke care as one of the core use cases for its Swoop portable MRI system. Noting that, as with Viz.ai’s care coordination platform, its use should enable patients to receive treatment sooner, with symptomatic patients able to be imaged right away in emergency rooms or even in ambulances ensuring that they are placed on the correct care pathway and provided the right treatment as quickly as possible.

As such, Hyperfine had already been developing its own neurology imaging AI solution. BrainInsight, Hyperfine’s neurological AI solution, is centred around measurement and quantification, allowing biomarkers signifying neurological damage to be assessed, for example, midline shift following a suspected stroke. BrainInsight will complement Viz.ai’s capabilities and care coordination platform. Adding Viz’s solution to Hyperfine’s Swoop will make the system a more versatile offering, and a more valuable tool for doctors in fast-paced settings such as emergency medicine.

Partnering with Viz.ai also offers other benefits. Several other vendors have targeted the edge deployment route, including Exo and Butterfly Networks (point of care ultrasound), and established brands such as Fujifilm (portable X-ray), who offering image-analysis AI capabilities from Medo.ai, Ultromics and Annalise respectively. While Hyperfine does offer a native AI solution, this partnership will quickly add greater functionality to its scanner.

In addition, it also enables Hyperfine, which is primarily focused on hardware, to quickly scale up the availability and deployment of AI solutions for its modality. This is an important consideration given that in many cases, young challenger vendors offering mobile imaging solutions often sacrifice some image quality in the name of mobility. Such a compromise can, however, be mitigated with the use of AI, enabling these devices to be used for tasks that may otherwise have been beyond the hardware’s limits.

Partnership Planning

At present, partnerships are an effective way for both hardware and software companies to benefit from such collaborations. While in some cases, acquisitions, such as Exo’s acquisition of Medo.ai make sense, allowing the hardware vendor granular control over the AI vendor and enabling it to be specifically tailored, often partnership presents a better route.

This is true in the case of Hyperfine and Viz.ai, with Hyperfine a long way from being able to afford large acquisitions. Even without this financial hurdle, a partnership is likely preferable for both vendors, enabling them to easily enter into an agreement and capitalise on the other’s strengths without needing to commit for the long term if a better option becomes available. For Hyperfine it means it can offer Viz.ai’s solution, while Viz.ai can expand onto another potential growth platform for minimal cost.

Despite its recent valuation, Viz.ai is also unlikely to want to acquire Hyperfine. The fact it remains a nascent, yet innovative, technology, with a relatively modest installed base is reason enough to deter Viz.ai. More broadly, Viz.ai’s other recent partnerships also suggests Viz is not looking to acquire just yet.

Both vendors could also look to make more partnerships. Viz could look to secure more partnerships with other modality vendors, with a more established modality vendor with a sizable installed base a significant opportunity. Such a partnership may be difficult to come by, but it would give the vendor enhanced credibility and visibility among providers.

Hyperfine is also likely to look to embark on additional partnerships. While neurology is one of the vendor’s present priorities, the ability of AI solutions to improve the suitability of the Swoop for other clinical use cases and mitigate some of the limitations of the hardware for certain applications could bring opportunity, in prostate imaging, for example. By partnering with multiple AI vendors, Hyperfine can improve the value proposition of its Swoop, ensuring it is a more versatile system which can be utilised for many use cases by providers. Further, this ensures it can remain focused on developing and fine-tuning its hardware, rather than splitting its focus to develop AI solutions as well.

Such benefits will, however, take time to be realised. The partnership between the two vendors will not be transformative, particularly for Viz, with use on Hyperfine’s portable MRI systems offering limited potential, at least in the near term. It does however make sense, given the low cost of the partnership. The move, as with other hardware/AI partnerships before it, show the synergies that can be found and AI’s ability to maximise hardware’s capability. It may not ultimately lead to a dramatic increase in sales for either company, but other similar future partnerships might.

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This Insight is part of your subscription to Signify Premium Insights – Medical Imaging. This content is only available to individuals with an active account for this paid-for service and is the copyright of Signify Research. Content cannot be shared or distributed to non-subscribers or other third parties without express written consent from Signify ResearchTo view other recent Premium Insights that are part of the service please click here

Signify Premium Insight: Of Planes and Purple Cows: MaxQ AI’s Failure Under Pressure

This Insight is part of your subscription to Signify Premium Insights – Medical ImagingThis content is only available to individuals with an active account for this paid-for service and is the copyright of Signify Research. Content cannot be shared or distributed to non-subscribers or other third parties without express written consent from Signify ResearchTo view other recent Premium Insights that are part of the service please click here.

Co-written by Dr Sanjay Parekh

Earlier this month, Israeli AI vendor MaxQ AI (MaxQ) announced that it was axing its Accipio range of products for the detection and triage of intracranial haemorrhage (ICH) and completely ceasing the development of image analysis-based AI applications.

The company, which was founded in 2013, will continue to exist, but will instead focus on non-image-based algorithms that look to use vast amounts of medical data to identify anomalies that cause poor clinical outcomes or clinical inefficiencies. However, the pivot has resulted in Accipio sales and marketing personnel being laid off with immediate effect

The Signify View

“We have had a history of losses and we may be unable to generate revenues” warned MaxQ in an investor prospectus from 2018 as it set about an ill-fated attempt to list on the Nasdaq Capital Market. This warning was printed in the chapter of the prospectus entitled ‘Risk Factors’, a chapter which, with the benefit of hindsight, is sadly prophetic. Among those identified risks which proved particularly close to the mark were “failure to articulate the perceived benefits of our solution or failure to persuade potential…customers that such benefits justify the additional cost”; “ Failure to generate broad customer acceptance of or interest in our solutions,”; and the “introduction of competitive offerings by other companies”. These factors and others were instrumental in the failure of MaxQ’s Accipio products, with some aspects more important than others.

Perhaps the most significant of MaxQ’s weaknesses was the Accipio range itself. When the company launched in 2013 as MedyMatch its vision of a product, which was focused on detecting an intracerebral hemorrhage (ICH), was at the forefront of medical imaging AI. In 2022, however, solutions are much more mature. Products from other vendors offering stroke imaging solutions such as RapidAI and Viz.ai address both ICH and large vascular occlusion (LVO), but also add value along the clinical pathway. Instead of focusing solely on detection, these more sophisticated solutions (care coordination platforms as previously described by Signify Research) add other functionality such as triage capability, perfusion quantification, mobile viewer and prehospital workflow applications, and secure care coordination tools. In comparison, other tools from MaxQ never made it to market. There were additional tools in development, but the vendor has been commercially reliant on its Accipio Ix and Ax tools focused only on identification and prioritisation, and slice level annotation and prioritisation respectively. The company had also struggled to obtain US-FDA clearance, a necessity to gaining a foothold in the US, a market dominated by RapidAI and Viz.ai.

Ultimately, for AI solutions to be attractive to providers they must offer them greater clinical value than is offered by the narrow Accipio tools. There are some use cases where narrow AI tools do make sense, such as FFR-CT, but more frequently AI developers need to add additional capability along or across the workflow to make solutions worthy of a provider’s spend. With such competition in the stroke detection market, it was inevitable that those with the weakest value propositions would, sooner or later, falter.

An Appropriate Model?

Another challenging factor contributing to MaxQ’s retreat was its business model, which was highly reliant on channel partnerships.

In some cases, there are advantages of a sales strategy centred around these partnerships. Such setups, for example, can allow vendors to scale very rapidly as they are tapping into an existent customer base. They can also help to establish a young vendor’s reputation, with a partnership from a long-established and well-trusted vendor bestowing credibility upon an unknown developer. However, there is a price to pay for these benefits, with a vendor being dependent on an external sales team. Radiology AI, as a very young market, hasn’t yet become a priority for the vendors charged with selling MaxQ’s software, especially if it risked delaying the sale of a modality scanner or imaging IT software. As such, those vendors’ sales teams would also be unlikely to prioritise the software and promote it as effectively as a direct sales team might.

Another challenge comes in the form of market education. This remains one of the barriers for the medical imaging AI market for AI vendors themselves, let alone a channel partner attempting to convince a potential customer. It is hard to convince providers to allocate budget on any new and untested technology, but this persuasion is made considerably more difficult if a sales team doesn’t have a complete understanding of the product they are promoting. While those vendors selling MaxQ’s products would have an appreciation of the technology, it is unlikely that they would have the same level of nuanced understanding, or the same easy access to additional information as a direct sales team could possess.

Sales Are More Than Transactions

These challenges mean that even under a channel partnership model, an AI developer must still allocate significant resource into the promotion of its products. One example of a vendor that has done this well is Lunit, a vendor who has recently crossed into the ‘$100m club’ of vendors that have secured more than $100m in capital funding. Although it also utilises a channel partnership model, Lunit has also pursued direct sales in its native South Korea, and also invested heavily in clinical validation studies. It has then exploited these studies, to convince sceptical providers of its value. In combination it has also been a steady presence at RSNA and other meetings, and a frequent contributor to expert panels and lecterns at conferences. Even when other partner vendors have sealed transactions, Lunit has been very active in the selling.

For MaxQ this job was made harder still by the limited clinical validation it was able to undertake, which led to the withdrawal of its US-FDA approval for detection. While the product was still approved for use as a prioritisation tool, the lack of FDA approval for its detection capabilities would no doubt have raised doubts in a potential customer’s mind, particularly as other vendors were securing a number of full regulatory approvals, and even in some cases, reimbursement.

MaxQ last secured funding in March 2019 of $30m, at the time a very healthy figure. This however followed the vendor’s aborted attempt to list in 2018, which was set to raise a comparatively small figure of $8m, suggesting an urgent need for cash. This begs the question, if more capital had been raised would MaxQ have been able to overcome the challenges it faced? It would no doubt have helped, but continued investment needs to be earned, and MaxQ, despite its very early entry onto the market, and early de novo FDA approval failed to gain traction. Seth Godin’s Purple Cow marketing theory emphasises the importance of being remarkable (as in the titular bovine) in being noticed. MaxQ AI was remarkable in its earliest days, but as time passed and other more sophisticated solutions were released from other vendors, the Accipio line of products failed to hold interest. MaxQ AI slowly slipped back into the pack.

The Point of Failure

“MaxQ is an aeronautic term that means maximum pressure, which is typically the point where failure occurs”, explained MaxQ AI’s then Chair and CEO, Gene Saragnese in an interview with AiThority in 2019. Sadly, for the Israeli vendor this point of failure has now arrived and, MaxQ AI has become one of the most significant pioneers to falter amidst the consolidatory pressures in the bourgeoning medical imaging AI market. While it is easy for survivors to smugly pore over MaxQ’s mistakes with the benefit of hindsight, many would do well to heed the warnings. There are several vendors that will, in the relatively near future, succumb to similar pressures. One need only look at the competition in some markets to see how challenging things are set to become. In the breast AI market, established leaders are making it increasingly difficult for less established vendors which lack unique products to gain any ground. The chest X-ray AI market, meanwhile has seen some technology leaders with increasingly comprehensive, and increasingly clinically valuable solutions emerge, throwing shade on other, once-promising vendors. Even AI for more advanced imaging, like brain MRI, is becoming increasingly homogenised, with several solutions that lack competitive differentiation appearing at risk of failure.

Consolidation in the radiology AI market is coming. There are simply too many vendors chasing too few dollars for it to be otherwise. Those vendors that will thrive in this consolidation are those that are able to differentiate their products from the competition, add considerable clinical value (beyond feature detection) and solve the pertinent problems that providers face (such as improving workflow efficiencies). Moreover, they must continue to innovate to remain remarkable.

It’s too late for MaxQ AI, but other vendors need to ensure they meet these criteria, lest they become another example left to be dissected.

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This Insight is part of your subscription to Signify Premium Insights – Medical Imaging. This content is only available to individuals with an active account for this paid-for service and is the copyright of Signify Research. Content cannot be shared or distributed to non-subscribers or other third parties without express written consent from Signify ResearchTo view other recent Premium Insights that are part of the service please click here