Strengthening Community Health Representatives for AI Echo Screening
Presented at AHA 2026, this abstract reports 485 AI echo screening studies in a rural American Indian community and a Training-of-Trainers model for local mentors.
Publication Library
AI Echo and Biomarkers for Heart Failure Diagnosis Across the Ejection Fraction Spectrum
This population-based study of 578 patients and controls from Scotland combined Us2.ai automated echocardiographic analysis with nine blood biomarkers to characterise heart failure across the ejection fraction spectrum. Us2.ai delivered comprehensive cardiac phenotyping across HFrEF, HFpEF and controls, while all nine biomarkers discriminated heart failure from controls with AUCs of 0.90 to 0.97.
AI Echo for Longitudinal Monitoring of ATTR Cardiomyopathy
Published in the International Journal of Cardiovascular Imaging, this study from University Hospital Zurich demonstrates that Us2.ai-derived echocardiographic measurements show within-study repeatability comparable to that of experienced human readers in patients with ATTR cardiomyopathy, supporting further prospective evaluation of AI-assisted serial echocardiographic assessment in this population.
AI Echo is able to Detect HCM, Discriminate Between Phenocopies and Quantify LVOT Obstruction
Presented as a late-breaking clinical science presentation at ESC Congress 2026, this multinational study demonstrates that a fully automated end-to-end deep learning system detects HCM from routine echocardiography with 87.4% external sensitivity, exceeding the published expert benchmark by 37 percentage points, while also discriminating phenocopies and quantifying LVOT obstruction.
AI Detects Pulmonary Hypertension from a Single Echo Clip
Presented as a late-breaking clinical science presentation at ESC Congress 2026, this multinational study demonstrates that a spatiotemporal transformer trained on the largest RHC-confirmed cohort to date can accurately detect pulmonary hypertension from a single apical four-chamber echocardiographic clip, with no Doppler or multi-view input required, achieving AUROC of up to 0.917 on external validation.
AI Deep Learning Outperforms Wall Thickness Score for Cardiac Amyloidosis Screening
Presented at ESC Congress 2026, this study from Tokushima University Hospital, Japan, demonstrates that the Us2.ai deep learning cardiac amyloidosis detection model achieved 98% sensitivity and a c-index of 0.82, substantially outperforming an automated wall thickness score in a biopsy-proven cohort.
AI Echo for Cancer Therapy-Related Cardiac Dysfunction in Breast Cancer: CARE Study
Presented at ESC Congress 2026, this study from the CARE trial at Akershus University Hospital demonstrates that Us2.ai reliably reproduces longitudinal LVEF and GLS trajectories and rules out CTRCD in a large real-world breast cancer cohort, supporting its potential to improve precision and workflow efficiency in cardio-oncology.
AI Echo For Cardio-Oncology: Accuracy, Reproducibility and Time Savings
Presented at ESC Congress 2026, this study from Royal Brompton Hospital validates Us2.ai in 384 patients from a dedicated cardio-oncology centre, demonstrating strong agreement with conventional echocardiography, improved interstudy repeatability, and a 43% reduction in study completion time.
AI Echo Helps Detect Treatment Response in ATTR Cardiomyopathy Over Time
Presented at ESC Congress 2026, this study from University Hospital Zurich, Bern University Hospital, demonstrates that Us2.ai-powered echocardiography can detect early attenuation of disease progression following disease-modifying therapy in ATTR-CM, supporting its use as a sensitive monitoring tool.
AI Echo for Prognostic Risk Stratification in Cardiac Amyloidosis
Presented at ESC Congress 2026, this study from University Hospital Zurich and Bern University Hospital demonstrates that Us2.ai-derived echocardiographic measurements provide robust prognostic stratification in cardiac amyloidosis, with performance comparable to manual measurements and incremental value over biomarker-based staging.
AI Echocardiographic Analysis and Mortality Prediction in Hospitalized Patients
This retrospective study of 889 hospitalized patients demonstrates that Us2.ai-derived echocardiographic measurements are strongly correlated with human expert assessments and comparably predictive of one-year mortality, with AI-derived left ventricular strain analysis providing superior mortality prediction over human assessment alone.
AI Echo Reduces Study Time by 35% in A Real-World Echo Lab
Published in the Journal of the American Society of Echocardiography, this mixed-methods time-motion study from Endeavor Health demonstrates that an AI-driven echocardiography workflow using Us2.ai reduced total study time by 35% across inpatient and outpatient settings, while all participating sonographers reported lower click burden and a preference for future AI adoption.
AI Echo for Community Heart Failure Screening: The Heart2Miss Initiative
Published in the European Heart Journal - Digital Health, the Heart2Miss initiative demonstrates that Us2.ai-powered AI-POCUS, operated by novice biomedical graduates after just four weeks of training, can detect previously undiagnosed heart failure in a high-risk diabetes population while reducing tertiary diagnostic burden by nearly 90%.
AI Echo Reduces Reporting Time in Outpatient Lab
Presented at the Canadian Society of Echocardiography Echo Weekend 2026, this prospective real-world study from Toronto Heart Center demonstrates that integrating Us2.ai into a community outpatient echocardiography laboratory reduced preliminary reporting time by approximately seven minutes per study while maintaining clinically acceptable agreement with sonographer-derived measurements.
AI Echo for Post-AMI Risk Stratification: Predicting Outcomes After A Heart Attack
Published in the International Journal of Cardiology, this study of 1,001 AMI patients demonstrates that combining AI-derived echocardiographic parameters with traditional clinical risk factors significantly improves prediction of one-year mortality and major adverse cardiac events, outperforming clinical risk scores alone.
AI Echo in Cardio-Oncology: Validation in a Cancer Population
Presented at ESC Cardio-Oncology 2026, this study from Royal Brompton Hospital validates the use of AI echo in a dedicated cardio-oncology population, demonstrating strong agreement with conventional echocardiography for LVEF assessment and high accuracy for detecting clinically relevant LV dysfunction across key cancer subgroups.
Integrating Clinical Data with AI Echocardiography for Improved Cardiac Amyloidosis Detection
Published in Circulation: Cardiovascular Imaging, this international multicentre study demonstrates that integrating clinical and laboratory data with the FDA-cleared Us2.Ca deep learning model significantly improves cardiac amyloidosis detection, achieving 93% sensitivity and 90% accuracy while eliminating indeterminate classifications entirely.
AI-Assisted Heart Failure Diagnosis: An Economic Evaluation
Published in ESC Heart Failure, this economic evaluation from Singapore demonstrates that deploying US2.ai on a novice-operated point-of-care device is cost-saving compared to conventional sonographer-led echocardiography, with a 99.9% probability of cost savings and an average reduction of S$1,185 per patient.
AI Echo for LVEF Assessment in Geriatric Patients
A prospective study presented at Heart Failure Congress 2026 demonstrates that AI-assisted echocardiography using the Us2.ai software can accurately assess left ventricular ejection fraction in geriatric patients, even when performed by a geriatrician with minimal echo training.
PANACEA-HF: Practice nurse-led AI-PoCUS detects undetected heart failure in primary care
PANACEA-HF study shows practice nurses can reliably apply AI-powered portable cardiac ultrasound to detect previously undetected heart failure in primary care patients aged ≥60. Presented at Heart Failure Congress 2026.
PANACEA-HF: Clinical algorithm optimizes detection of undiagnosed heart failure in primary care
PANACEA-HF study demonstrates a pragmatic clinical algorithm achieves 96.7% sensitivity and 98.8% specificity in detecting undiagnosed heart failure in primary care, outperforming unaided GP referral. Presented at Heart Failure Congress 2026.
Heart2Miss AI-POCUS echo triage reduces carbon footprint
Heart2Miss study shows decentralized AI-powered POCUS triage at primary care reduces travel distance by 53% and carbon emissions by 39% vs. conventional tertiary cardiac pathways. Presented at Heart Failure Congress 2026.
AI Echo in Community Heart Failure Screening: Methods and Implementation of the Everton BEAT Breathlessness Project
Published in the European Heart Journal, this methods paper describes the design and implementation of the BHF-funded Everton BEAT Breathlessness Project, a one-stop community diagnostic hub in Liverpool that deploys US2.ai as its AI-assisted echocardiography platform. The paper outlines how the programme brings guideline-quality cardiac assessment to underserved populations through trained non-specialist operators, with a target of screening 1,500 patients over 12 months and initiating treatment within 60 minutes of arrival.
AI-Assisted Peri-Operative Echocardiography Assessment
In a prospective study across five tertiary hospitals, US2.ai demonstrated good to excellent agreement with expert clinicians across 10 echocardiographic parameters, correctly identifying 100% of pulmonary hypertension cases and all severe aortic stenosis cases. With automated reports generated in under two minutes, the findings support US2.ai as a practical tool for streamlining pre-operative cardiac assessment at scale.
Diagnosis of Cardiac Amyloidosis on Echocardiography Using Artificial Intelligence
This study evaluated the performance of AI-derived measurements and a deep-learning model to detect cardiac amyloidosis on echocardiography, addressing the diagnostic challenge of distinguishing it from other hypertrophic phenotypes.
Advancing Pulmonary Hypertension Assessment with Fully Automated AI Echo
Pulmonary hypertension (PH) requires careful evaluation with echocardiography, but traditional manual interpretation can be time-consuming and prone to variability. This study demonstrates that a fully automated deep learning workflow using Us2.ai can reliably assess PH.
Building Echo Competency for Novices: A Curriculum Design
Presented at AHA 2025, this project tackles a critical gap in cardiac care for American Indians.
Uncovering Hidden Cardiac Amyloidosis with AI Echo
The AI-SCREEN-CA study evaluated the real-world performance of artificial intelligence for detecting hidden cases of cardiac amyloidosis (CA) from routine echocardiograms.
AI Echo in Paediatric Cardiology: Multicenter Validation Study
Presented at AHA Scientific Sessions 2025, this study from five paediatric centres provides the first independent validation of Us2.ai in a paediatric population, demonstrating at least moderate agreement across all echocardiographic measurements in childhood cancer survivors compared to gold-standard core laboratory readings.
AI Echo to Track Disease Progression of Cardiac Amyloidosis
Clinicians from Erasmus Medical Center share their experiences integrating Us2.ai into daily workflows for monitoring cardiac amyloidosis disease progression.
Detection of HCM with AI Echo
At ASE 2025, Dr. Alenzi presented a Late Breaking Science titled "AI-Automated Detection of Hypertrophic Cardiomyopathy by Echocardiography: Training and External Validation." The study showcased Us2.ai's HCM detection model, validated internally on Duke data and externally on a Japanese multicentre cohort.
Improving Heart Health in American Indian Communities with AI Ech
Presented at ASE 2025, investigators partnered with tribal members to co-develop a culturally informed heart health program.
External Validation of Deep Learning-based Echo Detection of Cardiac Amyloidosis using a Global Multiethnic Population
This multicenter study validates Us2.ai's fully automated pattern recognition approach for identifying cardiac amyloidosis.
Multimodal AI for Cardiac Amyloidosis Diagnosis
This study combines Us2.ai's pattern recognition model with clinical and laboratory data (AI‑ECM in this study), to enhance diagnostic accuracy for cardiac amyloidosis.
Aortic Stenosis Detection and Classification with an AI Echo Solution
At ASE 2025, three new studies highlighted how AI-powered echo advances detection, classification, and risk assessment of aortic stenosis.
AI Handheld Echo for Preoperative Assessment
Presented at ASE 2025, evaluating handheld echocardiography with AI software for LV diastolic dysfunction assessment.
Transforming Echo with AI
Clinicians from Erasmus Medical Center Rotterdam and Wilhelmina Ziekenhuis Assen share their reflections integrating Us2.ai into daily workflows.
AI Echo Advances TAVI Research: The Role of COPD, Atrial Fibrillation, and Heart Failure in Symptom Improvement
A recent multicentre study published in Catheterization and Cardiovascular Interventions leveraged Us2.ai's deep learning-based echocardiography software to investigate how common comorbidities affect symptom improvement following TAVI, advancing our understanding of patient outcomes in aortic stenosis care.
AI Echo Automation Improves Workflow For Sonographers
This randomized crossover trial is the first to evaluate daily use of AI-based automated echocardiographic analysis in real-world practice.
AI Echo in Cardiovascular Disease Management
A comprehensive review examining how AI is applied across the echocardiographic workflow.
Heart Failure Diagnosis with AI Handheld Echo
Researchers tested fully automated AI analysis on handheld echocardiography in 867 patients with suspected heart failure.
AI in Echo: State-of-the-art Automated Measurement Techniques and Clinical Applications
Us2.ai featured in JMA Journal review as one of the leading fully automated solutions for echocardiographic measurement.
AI GLS Strain Imaging in Multi-Vendor Comparison Study
Comparing GLS results from 3 vendor-specific and 6 vendor-agnostic software solutions.
User Perceptions of AI Echo
Describing their experience piloting Us2.ai at the Echo Lab at Endeavor Health.
Cardiac Ultrasound Triage for Early HF Detection using AI Echo
The Heart2Miss screening programme conducted in Malaysia drew significant attention for its innovative approach to early heart failure detection
Integrating AI into an Echocardiography Department
Study from Bordeaux University Hospital evaluated real-world AI in echocardiography, analyzing nearly 900 scans.
AI Echo for Tricuspid Regurgitation Severity
Presented at ACC.25, AI-powered workflow for assessing tricuspid regurgitation severity.
Everton BEAT-Breathlessness Community Hub: How Football Can Help Save Lives
An innovative approach to early diagnosis of heart failure and COPD, leveraging Premier League football.
Fully automated AI for mitral regurgitation severity grading
Published in JACC: Cardiovascular Imaging, validated a fully automated AI workflow for grading MR severity.
Machine Learning Technology to Automate Thoracic Aorta Dimensions by Echocardiography
Machine learning approach for automated measurement of thoracic aorta dimensions using echocardiography.
AI and digital tools for design and execution of cardiovascular clinical trials
How AI and digital tools transform design and execution of cardiovascular clinical trials.
Patient attitudes towards AI-Echo
Understanding patient perspectives on AI-powered echocardiography analysis.
AI Echo to Monitor TAVI patients
Using Us2.ai's automated deep learning workflow to analyse echocardiographic images, this study found that while patients with severe aortic stenosis showed substantial symptomatic improvement one year after TAVI, this was not reflected in measurable changes in cardiac structure and function.
Digital tools in heart failure: addressing unmet needs
How digital tools including AI echocardiography address unmet needs in heart failure.
Fully automated AI echo vs 3D and human readers
Head-to-head comparison of fully automated AI echo vs 3D echo and expert human readers.
Echo AI heart failure diagnosis by novices
Novice operators can accurately diagnose heart failure using AI-assisted echocardiography.
AI echo automated heart failure detection and classification from electronic health records
Research from University of Dundee demonstrated feasibility of AI to automatically identify and classify patients with heart failure from EHR data.
Fully automated AI assessment of the LV with contrast echo
Study at Christ Hospital Health Network presented at ACC.24 showed excellent agreement between AI and human readers with contrast echocardiography.
AI echo reduces exam time by 70%
AI echocardiographic analysis reduces examination time by 70%, dramatically improving clinical workflow.
AI for remote cardiovascular care in Africa
AI echocardiography for remote cardiovascular care in resource-limited settings across Africa.
AI-powered home ultrasound
Exploring feasibility of AI-powered ultrasound for home-based cardiac monitoring.
AI echo for strain imaging
Evaluating AI-automated strain imaging analysis compared to traditional vendor-specific solutions.
Heart failure treatment advances
Reviewing advances in heart failure treatment and the role of AI diagnostics.
AI for automated detection of heart failure in electronic health records
AI to identify cases and classify types of heart failure (HFpEF or HFrEF) from routine EHR surveillance data, using Us2.ai automated echocardiographic analysis.
Aortic Stenosis AI Echo Assessment
Aortic stenosis published in JASE. First study of its kind demonstrating the capacity of AI Echo to provide automated AS severity assessment.
Echo AI prediction of mortality outcomes
AI echo mortality prediction published in eBioMedicine. Echo can facilitate risk stratification, complementing traditional markers for improved patient management.
AI cardiac ultrasound at home by supervised nurses: the CUMIN study
AI-enabled, home-based cardiac care by nurses: abstract presented at the ESC Heart Failure Congress 2023.
AI Echocardiography for Mitral Regurgitation Severity Grading
AI-based MR severity grading presentation at EACVI 2023. Grading MR severity is essential due to high prevalence and impact on patient outcomes.
AI echo to predict prognosis of transthyretin amyloid cardiomyopathy
AI echo for ATTR-CM prognosis. Automated analysis is fast and less variable than manual analysis.
Automated echo strain AI measurements
Strain is a sensitive measure of cardiac function but clinical application is limited. AI shows promise in automation.
AI Echo White Paper
Comprehensive white paper on AI-powered echocardiography technology and clinical applications.
AI POCUS plus AI echo reports provide fully automated echocardiography for novices conducting heart failure screening
AI echo reports with AI Point-of-Care ultrasound enable novices to perform heart failure screening.
Fully automated AI echo analysis: validated as interchangeable with human experts
AI interpretation of echocardiograms: a formal validation of echo analysis software published in Nature Communications.
AI auto strain echo reporting software for AI echocardiogram interpretation
Echo Strain AI: validation of AI auto strain AI-assisted echocardiography presented at the AHA Scientific Sessions 2022.
AI in echocardiography for AI echo diagnosis support
Machine learning for diastology and heart failure with preserved ejection fraction: editorial published in JASE
AI echo diagnosis support for heart failure screening and detection from electronic health records
AI echo disease detection at scale: University of Dundee poster on the detection and diagnosis of prevalent heart failure from EHR data.
AI POCUS – Handheld Echocardiography with Continuous-Wave Doppler: Implications for the Evaluation of Valvular Heart Disease
JASE publication on AI Point-of-care Ultrasound with Continuous-Wave Doppler Capability to evaluate valvular heart disease.
Cardiac imaging with AI: a validation of echo analysis software
Deep learning in echocardiography: an automated AI echo workflow validation presented at EuroEcho.
AI for echocardiography: a multicohort study of deep learning in echocardiography
AI echo reports: automated echo diagnosis support for interpretation of systolic and diastolic function.