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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.
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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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 Research. To view other recent Premium Insights that are part of the service please click here