Truway Health, Inc.
This prospective, multicenter study will evaluate a federated machine-learning system designed to analyze focused cardiac point-of-care ultrasound examinations. Federated learning allows participating clinical sites to contribute to model development while keeping raw ultrasound images and directly identifiable patient information within each site's controlled computing environment. Encrypted model updates, rather than patient images, will be transmitted for secure aggregation. The prospective validation cohort will include approximately 3,000 adults undergoing clinically indicated focused cardiac ultrasound. Model performance will be compared with an expert interpretation of a comprehensive transthoracic echocardiogram performed within 24 hours. The primary objective is to determine how accurately the model identifies reduced left ventricular systolic function, defined as a left ventricular ejection fraction below 40%. During the initial validation period, the investigational software will operate in silent mode. Its results will not be displayed to treating clinicians and will not be used to diagnose participants, select treatment, or replace standard clinical interpretation. The study will also evaluate image-quality classification, cardiac-view recognition, performance across clinical sites and ultrasound systems, model calibration, processing time, cybersecurity, privacy resilience, and performance across demographic and clinical subgroups. Long-term monitoring will assess whether model performance changes as clinical populations, ultrasound equipment, acquisition practices, and software environments evolve during the 2026-2037 study period.
Federated Learning for Point-of-Care Cardiac Ultrasound (FL-POCUS) is a prospective, multicenter clinical-performance study of a federated machine-learning system for focused cardiac point-of-care ultrasound. The study is intended to determine whether a diagnostic model can be developed and validated across multiple clinical environments without routinely transferring raw ultrasound images or directly identifiable participant information to a central training repository. Participating institutions may use previously collected, locally governed ultrasound examinations during the federated mode…
Inclusion Criteria: * Age 18 years or older. * Undergoing a clinically indicated point-of-care cardiac ultrasound (POCUS) examination at a participating site. * Reference transthoracic echocardiogram completed within 24 hours of the index POCUS examination. * At least one cardiac ultrasound view attempted during the index examination. * POCUS and reference echocardiography records can be linked using an authorized coded study identifier. * Participant consent obtained or inclusion authorized under an Institutional Review Board-approved consent waiver, as applicable. Exclusion Criteria: * Re…
A clinically indicated, noninvasive focused cardiac ultrasound examination performed through a point-of-care ultrasound system. Standard views may include parasternal long-axis, parasternal short-axis, apical four-chamber, and subcostal views. The examination will be evaluated for image quality, cardiac-view classification, left ventricular function, and evidence of reduced left ventricular ejection fraction.
Investigational software that analyzes focused cardiac point-of-care ultrasound examinations using a version-locked federated machine-learning model. The system evaluates cardiac-view classification, image quality, left ventricular function, and the probability of a left ventricular ejection fraction below 40%. During this observational validation study, all model outputs will remain in silent mode and will not influence clinical care. Raw ultrasound images and directly identifiable participant information will remain within each participating site's controlled environment.