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Co-written by Dr. Sanjay Parekh
Annalise AI recently announced that it has launched an AI-powered decision support solution for non-contrast CT brain studies. The Australian company boasts that its new solution, dubbed Annalise CTB, is the most clinically comprehensive brain CT solution available, and can identify 130 findings.
The tool continues along the strategic path established by Annalise with its comprehensive chest X-ray solution, with a product strategy focused on detection of all radiologically relevant findings for a given body area or scan type, therefore more closely mimicking a radiologist’s process. As such, Annalise hopes its comprehensive solutions will be more clinically useful than those offered by many of its peers, who have tended to provide ‘point’ solutions for a single or small group of radiological findings.
The Signify View
When Annalise AI launched Annalise CXR, its chest X-ray solution, it immediately attracted the attention of all who study the medical imaging AI market. It did so because of the number 124; the number of findings the solution could detect. While there is some nuance, this figure was markedly higher than that of the most comparable vendors operating in the market. At 124, rather than the 10, 20 or even 50 findings claimed by competitors, Annalise made clear it was adopting a different approach to medical imaging AI.
Many vendors, particularly in the earlier days of medical imaging AI, were preoccupied with improving the sensitivity and specificity of the detection of a single radiological finding. While there are some scenarios in which such competent ‘point’ solutions are valuable, for the most part they offered only incremental gains compared to an unassisted radiologist, while frequently disrupting that radiologist’s workflow. Furthermore, the radiologist would often still need to thoroughly read a scan, to look for every other finding that the algorithm was not searching for. The benefits, in short, often failed to outweigh the drawbacks.
Various approaches to this challenge were adopted by vendors, many sought to expand their product’s utility along a clinical workflow, turning their algorithms into just one component of an expanded solution. Annalise, on the other hand, sought to expand its breadth of capabilities across multiple findings. Significantly, aiming to detect all possible findings for a single modality/body area combination. In doing so, the tool would more closely resemble the approach of radiologists. This would offer greater clinical value, also helping identify incidental findings and expediting the read for a radiologist.
Headspace
As with Annalise’s CXR solution, this philosophy permeates the vendor’s head CT solution, which identifies numerous findings, including those related to the brain, such as brain bleeds and midline shifts, as well as to the head more broadly, with, for example, eye orbits and paranasal sinuses both assessed, as well as findings on the scalp and neck. This breadth expands upon the focus of many of Annalise’s competitors, which often only address findings for the brain. Annalise’s solution, which addresses a broader range, could therefore prove attractive to providers, particularly when some time-consuming reads, such as C-spine assessment, are considered.
Beyond tackling some of these exams that are less well catered for, the adoption of comprehensive solutions can also herald an approach more focused on population health. In mimicking a radiologist, comprehensive solutions can help avoid missing findings, particularly those that are not part of the primary diagnosis, enabling patients to be put on a treatment pathway for these incidentals as well as primary findings. In doing so, missed diagnoses or misdiagnoses can be reduced enabling patients to be treated sooner and outcomes to be improved.
While such population health advantages can be valuable, their impact will be most significant in single payer markets, where the payer and provider, represent the same entity. In such a system, regardless of where or when the patient continues treatment, any downstream savings made and any reduction in care costs over the longer term will ultimately benefit the same payer. Such an advantage cannot be conferred in predominantly private markets, where there is no guarantee that identifying additional findings in a scan will bring benefits to the same provider. Instead, allocating resource on an AI solution may only benefit a different provider, where the patient eventually seeks treatment.
Another similar challenge in private markets will be in convincing providers to utilise comprehensive solutions. Although they may have some clinical advantages, for providers it is often advantageous to conduct, and therefore bill for, multiple specialist scans, rather than a single, comprehensive scan. As such, there may be limited motivation among providers to adopt such tools.
Regulatory Burden
Adoption of Annalise’s Head CT solution, along with other comprehensive tools, also faces another challenge in the world’s largest private healthcare market, the US: regulation. While Annalise’s solutions have received regulatory clearance in Europe and Australia as a single comprehensive tool, in the US, the FDA has held-out, insisting that each of a solution’s findings are treated as if a narrow, point solution. Each finding must, in effect, be treated as a single product.
The rationale behind such an approach is that each individual finding promised by a comprehensive solution should be subjected to the same regulatory rigour as a point solution, thereby ensuring that a comprehensive solution can demonstrably perform as effectively as an approved point solution in any single task. In Europe, conversely, comprehensive solutions have been regulatorily palatable provided they meet a minimum viable threshold.
There are merits to each of these approaches. The US FDA’s demanding criteria is, in the short term at least, arguably good for clinical practice. It ensures patient safety, minimising the opportunity for misdiagnosis, and prioritises patient outcomes. But, it is a regulatory framework that will stifle innovation, and in the longer-term prevent US patients benefiting from some tools. While these high barriers are appropriate in some cases, such as common findings where there are considerable training data, they severely hamper vendors’ abilities to address more obscure findings targeted in Annalise’s CTB, in the paranasal sinuses or in the pineal gland, for example.
There are some routes that purveyors of comprehensive algorithms for more obscure findings can take in the US. They could, for example, seek approval via the triage (CADt) rather than detection (CADe) regulatory pathway; a more straightforward route to market. Another option for vendors offering comprehensive solutions is to break up their offerings in the US, only offering the specific algorithms which have been approved. Both these approaches may help a vendor get a toe in the market, but neither are ideal, both potentially robbing solutions of their strengths.
Diverging Details
Despite these drawbacks, these are the concessions that Annalise is likely to have to make if it seeks to gain ground in the US. There is no reason to expect that either of the vendor’s solutions are to be any less well received than its chest X-ray solution has been in Europe and Australia. However, if it is to establish a footprint in the US, the vendor will have to take on the more time-consuming piecemeal approach to approval, beginning with the most clinically common solutions, before working its way through its broader array of findings.
The ramifications of such a requirement could, over time be significant. In Europe and Australia, comprehensive solutions could flourish, becoming providers’ preferred methods of AI adoption. In the US however, the FDA’s approach could mean that platforms which offer one or multiple suites for certain clinical use cases could become the norm. Enabling a range of vendors, each focused on their own regulatory challenges, to effectively be offered together through a platform to provide hospitals with a useful breadth of capability.
Although in some sense these platforms appear antithetical to Annalise’s comprehensive approach, they could themselves be an opportunity. Like Aidoc before it, Annalise may choose to offer a platform itself, including a version of CXR and CTB, which has been cut down to secure regulatory approval, alongside some other solutions from partner vendors. Over time though, as Annalise receives approval for more findings, and releases other products with different focuses based on modality, body area or clinical focus, it could incorporate them into its own platform displacing third parties. As such, it could adopt a gradual approach to the US market while capitalising on the different regulatory frameworks elsewhere.
As ever, there are varying routes to success, and in this nascent market, there is no certainty about which one is preferable. Other leading vendors have made their mark in their own unique ways, with, for example, Heartflow and Cleerly changing the diagnostic pathway for diagnosing patients with coronary artery disease, Viz.ai, RapidAI, and others altering care pathways for stroke care, and Perspectum helping reduce the need for liver biopsies. Similarly, Annalise, with its head CT solution, emphasises its intent to be the top comprehensive solution.
Other vendors are following a similar path, some with considerable advantages in other settings – one only need look at Lunit’s breadth of partners to see the esteem in which that firm is held – but Annalise has ensured that providers must at least consider its comprehensive potential.
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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
