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news.Viz.ai has entered a collaboration with Alnylam to advance earlier identification and streamline diagnosis for cardiac amyloidosis, an underdiagnosed cause of heart failure.
The pathway uses the FDA-cleared echocardiography AI algorithm Us2.ai, integrated with EHR, and generative AI. Credit: raker / Shutterstock.com.
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Cardiac amyloidosis is a progressive condition where misfolded proteins accumulate in the heart muscle, often leading to heart failure.
There are two main types of cardiac amyloidosis: wild-type or hereditary transthyretin-mediated amyloidosis (ATTR-CM), and light chain amyloidosis (AL).
The partnership will see Viz.ai develop an AI Care Pathway for this condition, designed to bring patients to clinical attention sooner and assist clinicians through guideline-based steps.
This pathway uses the FDA-cleared echocardiography AI algorithm Us2.ai, integrated with electronic health records (EHR), and generative AI.
It automatically analyses standard echocardiograms, identifies patients with suggestive signs, and coordinates confirmatory testing, referral, and follow-up across cardiology teams.
For Alnylam, the collaboration supports ongoing efforts to improve ATTR-CM care by facilitating earlier diagnoses and strengthening coordinated care pathways.
The Viz Cardiac Amyloidosis Care Pathway will initially launch with a multi-site pilot trial to assess workflow integration and gather real-world evidence regarding its effect on patient identification, diagnosis timelines, treatment initiation, and overall care coordination.
It expands the Viz Cardio Suite, which is part of the larger Viz platform, currently deployed in 2,000 hospitals across the US.
Viz.ai’s chief medical officer Tim Showalter said: “Cardiac amyloidosis is a condition where delayed diagnosis has real consequences in heart failure – earlier identification can fundamentally alter a patient’s trajectory. This partnership is about making early detection actionable.
“By combining advanced imaging and generative AI with a care coordination platform, we will help clinicians find the right patients earlier and move them swiftly toward appropriate care before the window for meaningful intervention narrows.”
