Us2.ai is proud to have been featured in four presentations at the Heart Failure Congress 2026 in Barcelona, drawn from landmark studies including PANACEA-HF, a prospective, multicentre surveillance program investigating earlier detection of heart failure in Australian primary care; an environmental sub-study of Heart2Miss examining the carbon footprint of decentralised AI-powered cardiac triage; and a prospective study from Hospital Broca of Paris evaluating AI-assisted LVEF assessment in geriatric patients.

The presentations highlight how point-of-care ultrasound, coupled with Us2.ai's AI echo automated software, is being applied across a range of clinical settings. From practice nurses and GPs bringing cardiac imaging into everyday primary care, to geriatricians performing accurate LVEF assessments with minimal training, helping to lower the environmental footprint of cardiac diagnostics.

 

Presentations featuring Us2.ai:

  • Practice nurse-led use of portable echo to detect early forms of heart failure in primary care: insights from a prospective multicenter surveillance study — demonstrating that practice nurses can reliably generate clinically meaningful cardiac reports using Us2.ai software after structured training. Read more →

 

  • Operational characteristics of a clinical algorithm to optimize the detection of undiagnosed heart failure in primary care: insights from a prospective, multicenter study — examining how a structured screening pathway integrating Us2.ai AI echo automated software with point-of-care cardiac imaging performs in identifying and triaging previously undetected HF cases across metropolitan and rural primary care settings. Read more →

 

  • Can Decentralized Community Rapid Cardiac Ultrasound Triage Reduce Carbon Footprint? Environmental Insights From the Heart2Miss Study — finding that decentralised AI-powered triage at primary care level reduces patient travel by 53% and carbon emissions by 39% compared to conventional tertiary referral pathways. Read more →

 

  • Relevance and feasibility of artificial intelligence-assisted echocardiography for left ventricular ejection fraction assessment in geriatric patients — demonstrating that AI-assisted LVEF measurement using Us2.ai software achieved 99% sensitivity and 93% diagnostic accuracy when performed by geriatricians with limited echocardiography training. Read more →