ASE 2026 Late-Breaking Abstracts

ASE’s 2026 Scientific Sessions showcases innovative research in cardiovascular ultrasound focused on advancing patient care.

In addition to more than 500 original research abstract presentations, this year’s meeting will feature four late-breaking abstracts exploring emerging applications of artificial intelligence, novel approaches to evaluating severe aortic stenosis, and next generation ultrasound enhancing agents. The late-breaking abstracts’ methods, results, and conclusions will be presented during special sessions at ASE 2026.

• Building Deep Learning Model for Classification of Patent Foramen Ovale-Right to Left Shunt based on Contrast Transesophageal Echocardiography Image

  • Presented by Dr. Jian-bo Zhu, Shaoxing People’s Hospital
  • This research study developed an artificial intelligence model to automatically classify the severity of patent formen ovale (PFO) right-to-left shunts on contrast transesophageal echocardiography. The AI model demonstrated high accuracy and stability of right-to-left judgment and these findings suggest that the model has the potential to assist sonographers in making diagnoses.

• Prognostic Value of Echocardiography-Derived Pressure-Adjusted Heart Rate in Severe Aortic Stenosis

  • Presented by Dr. Jessia Tsai, Mayo Clinic
  • This research study evaluated pressure-adjusted heart rate (PAHR), defined as heart rate x (right atrial pressure / mean arterial pressure), as a prognostic marker in severe aortic stenosis (AS). The findings suggest that PAHR is a robust risk stratification tool that may help identify high-risk AS patients who could benefit from closer monitoring and earlier intervention.

• Evaluation of MVT-100: A Novel Ultrasound Enhancing Agent for Echocardiography with Improved Left Ventricular Opacification and Reduced Acoustic Shadowing Compared with Definity®

  • Presented by Dr. Evan Unger, Microvascular Therapeutics, Inc.
  • This research study evaluated the performance and safety of MVT-100, a novel perflutren-based ultrasound enhancing agent (UEA) designed to improve image quality while reducing shadowing and for room temperature storage. The prospective interventional study was registered with ClinicalTrials.gov and the findings suggest that MVT-100 has the potential to improve diagnostic accuracy and image quality in echocardiographic assessment.

• Barriers and Facilitators to Artificial Intelligence Adoption in Echocardiography Laboratories: A Qualitative Study of ASE Members

  • Presented by Dr. Vinay Guduguntla, Northwestern University
  • This prospective, multicenter, qualitative research study, sponsored by the ASE AI Taskforce, aimed to identify multilevel barriers and facilitators to artificial intelligence adoption in echocardiography laboratories and to inform a practical framework for its responsible implementation. The findings suggest that successful AI integration in echocardiography laboratories appear to be shaped by factors extending beyond algorithm performance alone. These insights may help to inform professional society recommendations, oversight and design of post-market surveillance models, targeted educational efforts, and future policy and reimbursement advocacy needed for the responsible, scalable, and equitable integration of AI into echocardiography settings.