Article | JAN 15, 2026
Pulmonary hypertension (PH) requires careful evaluation with echocardiography, but traditional manual interpretation can be time-consuming and prone to variability. This latest study demonstrates that a fully automated deep learning (DL) workflow using Us2.ai software can reliably assess PH, offering a faster, more consistent assessment.
In this study, the DL system was tested against expert core laboratory reads. Results showed minimal bias in key PH measurements such as peak tricuspid regurgitation velocity (TRV), right atrial area, and tricuspid annular plane systolic excursion. Importantly, the system maintained high accuracy in detecting PH, with area under the curve (AUC) values comparable to expert readings.
These findings indicate that Us2.ai’s fully automated echocardiography workflow can streamline PH assessment, reduce manual workload, and support more efficient clinical decision-making.
Celestin, B., Bagherzadeh, S. P., Santana, E., Frost, M., Iversen, M., Hermansson, F. N., Sweatt, A., Zamanian, R. T., Hummel, Y. M., Rendon, Gabriela. Gomez., Yen, J., Sandros, M., Salerno, M., & Haddad, F. (2025). Artificial Intelligence-Based Echocardiography in Pulmonary Arterial Hypertension. CHEST. https://doi.org/10.1016/j.chest.2025.06.052