Ten years after the first head-to-head comparison of two-dimensional speckle-tracking echocardiography derived GLS measurements obtained with software solutions from different vendors, the authors compared again GLS results from 3-vendor-specific and 6-vendor agnostic software solutions in a cohort of patients and volunteers with a wide range of LV function. Us2.ai’s Feature in this Study: Us2.ai was…
Describing their experience piloting the Us2.ai solution at the Echo Lab at Endeavor Health, this editorial shares positive feedback from both cardiologists and sonographers. Clinicians noted that AI can enhance measurement accuracy and improve workflow efficiency. Successful implementation depends on close collaboration with sonographers—empowering them to use AI as a supportive tool that streamlines their…
This study from Bordeaux University Hospital evaluated the real-world use of AI in echocardiography, analyzing nearly 900 echo scans across varying operator experience levels. Results showed strong agreement between AI-generated and human measurements—particularly for ejection fraction and Doppler-based parameters—highlighting AI’s potential to streamline workflow and reduce variability. The Us2.ai software was integrated into the hospital…
An innovative approach to early diagnosis of heart failure (HF) and COPD, leveraging the power of Premier League football ⚽ to reach communities that may not otherwise respond to community health care screening strategies. By integrating AI-enabled point-of-care diagnostics into a 1-stop community hub, this project is making early detection more accessible than ever. Sankaranarayanan,…
Published in JACC: Cardiovascular Imaging, the authors validated a fully automated AI echocardiographic workflow for grading Mitral Regurgitation (MR) severity. The study demonstrated that the machine learning approach was feasible, fast, highly accurate, and predicts 1-year mortality. Its implementation in clinical practice could improve patient care while improving quality and efficiency in echo labs. Sadeghpour,…
This study highlights the use of a convolutional neural network (CNN) [Us2.ai] to measure key thoracic aortic parameters—LVOT, SoV, STJ, and pAA—in Aortic Stenosis (AS) patients. The CNN demonstrated comparability to cardiologist measurements on transthoracic echocardiography (TTE) and computed tomographic angiography (CTA). With its ability to deliver rapid and highly reproducible measurements, CNN technology holds…
Digital tools, including AI-based tools, holds great potential to reshape the design and execution of cardiovascular clinical trials. In this review published in the European Heart Journal, the authors summarizes the landscape of digital tools at each stage of clinical trial planning and execution, and outlines roadblocks and opportunities for successful implementation in cardiovascular clinical…
Healthcare AI technologies have seen tremendous adoption in the medical imaging field. AI-echo’s performance has been shown to match or exceed human experts in many studies, but do we know if patients are ready to accept AI technology? In 100 patients with suspected heart failure who presented for cardiac imaging at National Heart Centre Singapore,…
Echo is essential in cardiovascular medicine for screening, diagnosis, and monitoring. AI has the potential to reduce variability and analysis time. While 3D echo is becoming more accurate, 2D imaging still dominates clinical care. This study evaluates agreement in measures of LV volume and function between human readers, Us2.ai and the 3D Heart Model.
The CUMIN study, published in European Heart Journal Digital Health, showcases the technical feasibility of AI-POCUS with an EchoNous Kosmos in the hands of novice nurses and opens new possibilities for redefining how we approach cardiac care, particularly in regions with limited resources. The fusion of AI, POCUS and nursing expertise holds the promise of…
The first fully automated AI software with validated global longitudinal strain in patients with and without atrial fibrillation; plus regional strain validated in real-world datasets; plus accurate identification of patients with heart failure, as well as those with regional wall-motion abnormalities, published in the European Heart Journal’s Digital Health.