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Flag Suspected Cardiac Amyloidosis on Routine Echo

Cardiac amyloidosis is underdiagnosed. Us2.ca, an FDA-cleared deep learning module, flags echo studies suggestive of cardiac amyloidosis in adults with increased wall thickness, alongside automated guideline-based measurements, so the clinician can decide on confirmatory testing.

A Common Disease Hiding in Plain Sight

Cardiac amyloidosis was once considered rare. Studies using cardiac scintigraphy now find it in a measurable share of some cardiac populations.

13%
of 120 patients admitted with HFpEF, in one study

Wild-type ATTR amyloidosis in patients aged 60 or over admitted with heart failure with preserved ejection fraction and a wall thickness of 12 mm or more (González-López et al., Eur Heart J 2015).

16%
of 151 patients undergoing TAVR, in one study

ATTR cardiac amyloidosis in patients with severe aortic stenosis undergoing transcatheter valve replacement (Castaño et al., Eur Heart J 2017).

Stage
at diagnosis shapes the options

Disease-modifying treatments work best in early-stage disease, and survival falls with each stage at diagnosis (ESC position statement, 2021).

The diagnostic gap is closing.

International guidelines now recommend screening for cardiac amyloidosis in patients over 65 with unexplained increased wall thickness1, a population that routinely undergoes echocardiography. Non-invasive diagnosis of ATTR is possible via bone scintigraphy, but only if the disease is suspected in the first place.

Deep Learning on a Standard Four-Chamber Clip

Us2.ai's deep learning model analyzes a standard apical four-chamber clip and flags studies suggestive of cardiac amyloidosis for a qualified clinician to review. No manual scoring. FDA cleared and CE marked.

Pattern Recognition from a Single View

The model reads the moving four-chamber image and reports one of three results: suggestive of cardiac amyloidosis, equivocal, or not suggestive. It does not mark where in the image the result comes from, and it does not tell amyloidosis types apart, which is why the measured red flags below sit beside it.

FDA Cleared for Amyloidosis Detection

This specific algorithm has received FDA clearance (Us2.ca, K250151) and CE marking for cardiac amyloidosis detection. Its FDA validation was a retrospective, multi-center study at sites in the United States and Japan, on images from GE and Philips ultrasound systems. It runs inside the same automated, guideline-based measurement and report as the rest of the study.

Built Into the Automated Report

Once the module is enabled, there is no extra workflow. Us2.v2 checks the images first and passes suitable four-chamber clips to Us2.ca, and the result appears among the report's main findings, where the clinician can clear or change it before signing.

Every Red-Flag Parameter, Measured and Shown

Beyond the AI flag, Us2.ai measures the parameters in the internationally recommended diagnostic criteria, so a clinician can check the flag against them before signing.

1

Increased Wall Thickness

LV wall thickness ≥ 12 mm in a non-dilated ventricle is the hallmark finding that triggers further workup. Us2.ai measures this automatically on every study, and it is the threshold that decides whether the Us2.ca result applies.

Primary Red Flag
2

Reduced Global Longitudinal Strain

GLS worse than −15% with a characteristic apical sparing pattern is highly suggestive. Us2.ai computes GLS and the apex-to-base strain ratio automatically.

Strain Analysis
3

Diastolic Dysfunction

Grade 2 or worse diastolic dysfunction with E/e' > 11 is a core diagnostic criterion. Us2.ai performs complete diastolic assessment including tissue Doppler velocities.

Diastolic Function
4

RV Dysfunction

TAPSE ≤ 19 mm indicates right ventricular involvement and carries prognostic significance. Automatically measured and reported by Us2.ai.

Right Heart
Multiparametric Scoring

A validated echocardiographic scoring system combines relative wall thickness, E/e', TAPSE, GLS, and apical sparing ratio to identify cardiac amyloidosis with high accuracy. Us2.ai delivers all five parameters in every automated report, providing the data clinicians need to apply this scoring at scale. The automated detection workflow for cardiac amyloidosis and hypertrophic cardiomyopathy is patented (US 12,400,762 B2).

How Us2.ca Performed in Its Cleared Validation

From the US user guide: Us2.ca was developed on 4,371 patients and externally validated on an independent set of 1,647 patients, in adults with increased left ventricular wall thickness.

87.1% Confident Results

Us2.ca returned a confident result for 1,434 of the 1,647 patients. The rest did not receive a confident result and were left for the clinician to assess.

Sensitivity 86.9%, Specificity 87.4%

In the patients with a confident result: sensitivity 86.9% (95% CI 84.2 to 89.7) and specificity 87.4% (95% CI 85.2 to 89.7), against patients with and without cardiac amyloidosis.

Validated on GE and Philips

External validation used images from GE and Philips systems, so accuracy may be lower on other systems. Us2.ca does not distinguish ATTR from AL amyloidosis.

AI-SCREEN-CA: Screening 11,000+ Archived Echos

The AI-SCREEN-CA study independently screened archived echocardiograms from more than 11,000 consecutive patients, a broader population than the labeled one, and was a late-breaking presentation at the American Heart Association Scientific Sessions 2025. It is not yet a peer-reviewed paper.

Late-Breaking Presentation · AHA 2025

AI-SCREEN-CA: AI-Assisted Screening for Cardiac Amyloidosis

Sakamoto, Kagiyama et al. American Heart Association Scientific Sessions 2025, New Orleans, LA

11,000+
Consecutive Patients
82.8%
Sensitivity
95.2%
Specificity

AI screen-positive patients had a cardiac amyloidosis prevalence over five times higher than screen-negative patients (16.7% vs. 3.0%). Screening a broad population is a research use; as labeled, Us2.ca is for adults with an IVSd or LVPWd of 12 mm or more.

Why Prompt Diagnosis Matters

Effective therapies now exist for cardiac amyloidosis, and they work best when started early. Us2.ca supports the clinician's decision on confirmatory testing; it does not claim an effect on outcomes.

Disease-Modifying Therapy

Clinical trials have demonstrated that approved disease-modifying therapies can reduce mortality and cardiovascular hospitalizations in ATTR cardiomyopathy. Treatment is most effective in early-stage disease, making timely detection critical.

Non-Invasive Diagnosis Now Possible

ATTR cardiomyopathy can be diagnosed without biopsy using bone scintigraphy, but only if suspicion is raised first. Echocardiography is the front-line imaging modality that triggers the diagnostic pathway.

Calls for AI Screening

The ESC Working Group on Myocardial Diseases2 explicitly identifies AI-based imaging tools as a key area of investigation to improve cardiac amyloidosis diagnosis at scale.

References

  1. Garcia-Pavia P, et al. Diagnosis and treatment of cardiac amyloidosis: a position statement of the ESC Working Group on Myocardial and Pericardial Diseases. European Journal of Heart Failure. 2021;23(4):512–526.
  2. Rapezzi C, et al. ESC Working Group on Myocardial and Pericardial Diseases position statement on the role of AI in cardiac amyloidosis. European Heart Journal. 2023.

Selected AI Echo Algorithm in Alnylam and Viz.ai's ATTR-CM Care Pathway

Us2.ai has been selected as the echocardiography AI algorithm powering a strategic collaboration to build an AI-enabled ATTR-CM care pathway, designed to help clinicians identify patients earlier in the course of disease and guide appropriate diagnostic evaluation and referral.

Alnylam Pharmaceuticals & Viz.ai

AI-Enabled ATTR-CM Care Pathway: Earlier Detection and Coordinated Care Across Health Systems

AWARE Study Prospective Implementation • Multi-system • ATTR Cardiomyopathy
“By embedding AI-driven detection into everyday clinical workflows and pairing it with coordinated care pathways, this collaboration focuses on closing the gap between the first clinical signal and meaningful clinical action.”
Tim Showalter, MD Chief Medical Officer, Viz.ai
Software Used For Flagging suspected cardiac amyloidosis on echo, ahead of ATTR-CM evaluation
Care Setting Routine clinical echocardiography workflows
Integration EHR-connected, integrated into existing care pathways
Scale 5 pilot health systems in the United States, launching 2026

Frequently Asked Questions

Common questions about cardiac amyloidosis and AI echocardiography.

How common is cardiac amyloidosis?

More common in some cardiac populations than once thought. Using cardiac scintigraphy, one study found wild-type ATTR amyloidosis in 13% of 120 patients aged 60 or over admitted with HFpEF and a wall thickness of 12 mm or more (González-López et al., Eur Heart J 2015). Another found ATTR cardiac amyloidosis in 16% of 151 patients with severe aortic stenosis undergoing TAVR (Castaño et al., Eur Heart J 2017).

How does echocardiography detect cardiac amyloidosis?

Key echocardiographic markers include increased LV wall thickness (≥12mm), reduced global longitudinal strain with apical sparing pattern, diastolic dysfunction (Grade 2+), reduced tissue Doppler velocities, and decreased TAPSE. A validated multiparametric scoring system combines these markers for diagnosis.

Can AI help detect cardiac amyloidosis?

Yes. Us2.ca, an FDA-cleared add-on module, analyzes a standard apical four-chamber clip and reports whether a study is suggestive of cardiac amyloidosis, equivocal, or not suggestive. It is intended for adults whose septal or posterior wall thickness (IVSd or LVPWd) measures 12 mm or more, and it supports a qualified clinician's diagnosis rather than making one. Us2.ai also measures the echo parameters in the internationally recommended diagnostic criteria (wall thickness, GLS with apical sparing, diastolic function, and TAPSE), so the clinician can check the result against them.

Why does early detection of cardiac amyloidosis matter?

Effective treatments now exist, including tafamidis for ATTR cardiomyopathy, but they work best in early-stage disease. Survival for both AL and ATTR amyloidosis falls with each stage at diagnosis (ESC position statement, 2021). Us2.ca flags studies suggestive of cardiac amyloidosis; it does not distinguish AL from ATTR, and it does not change survival on its own.

What Cardiologists Say About AI Amyloidosis Detection

Cardiac amyloidosis remains a frequently under-diagnosed condition with significant prognostic implications. Us2.ai's AI-based pattern recognition model, applied to a standard apical four-chamber view, provides a validated and scalable solution to support early detection and enhance clinical decision-making within routine echocardiographic practice.

As labeled, Us2.ca is for adults with an IVSd or LVPWd of 12 mm or more, supports the clinician's diagnosis and does not replace it. Population screening is a research use. Intended use

Marianna Fontana, MD, PhD
Professor of Cardiology and Honorary Consultant Cardiologist, National Amyloidosis Centre, University College London
Us2.ai's platform already provides powerful automated measurements that support the detection of cardiac amyloidosis. But this new deep learning approach moves beyond standard measurement workflows by enabling fast, fully automated detection of amyloidosis from a single echo view. By simplifying image requirements while maintaining diagnostic accuracy, it has the potential to expand the role of echocardiography as an accessible, practical screening tool for earlier identification of patients with this life threatening yet treatable disease.

As labeled, Us2.ca is for adults with an IVSd or LVPWd of 12 mm or more, supports the clinician's diagnosis and does not replace it. Population screening is a research use. Intended use

Jeremy Slivnick, MD
Assistant Professor, Division of Cardiovascular Medicine, The University of Chicago Medicine
Read more testimonials

A routine echo is where suspicion of cardiac amyloidosis starts.

Raise Suspicion Earlier, on the Echo You Already Run

Us2.ca flags possible cardiac amyloidosis in adults with a wall thickness of 12 mm or more, in the same automated report as the guideline-based measurements a clinician checks it against. The clinician makes the diagnosis.

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