101. Deep Learning-Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution
- Author
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Randolph P. Martin, Ali Chaudhry, Muhamed Saric, Rachel Liu, Samuel Surette, Carolyn Philips, Sara Nikravan, James D. Thomas, Victor Mor-Avi, Jose L. Diaz-Gomez, Steven A. Goldstein, Brandon Boesch, Russ Horowitz, Daniel Park, Issam Mikati, Nicolas Poilvert, Ha Hong, Federico M. Asch, David Rubenson, and Roberto M. Lang
- Subjects
Adult ,Male ,medicine.medical_specialty ,Heart Ventricles ,Point-of-Care Systems ,Echocardiography, Three-Dimensional ,030204 cardiovascular system & hematology ,Ventricular Function, Left ,03 medical and health sciences ,Young Adult ,0302 clinical medicine ,Deep Learning ,Internal medicine ,Medicine ,Humans ,Radiology, Nuclear Medicine and imaging ,030212 general & internal medicine ,Prospective Studies ,Point of care ,Aged ,Aged, 80 and over ,Ejection fraction ,business.industry ,Deep learning ,Reproducibility of Results ,Stroke Volume ,Middle Aged ,Cardiology ,Female ,Artificial intelligence ,Cardiology and Cardiovascular Medicine ,business ,Algorithms - Abstract
Background: We have recently tested an automated machine-learning algorithm that quantifies left ventricular (LV) ejection fraction (EF) from guidelines-recommended apical views. However, in the point-of-care (POC) setting, apical 2-chamber views are often difficult to obtain, limiting the usefulness of this approach. Since most POC physicians often rely on visual assessment of apical 4-chamber and parasternal long-axis views, our algorithm was adapted to use either one of these 3 views or any combination. This study aimed to (1) test the accuracy of these automated estimates; (2) determine whether they could be used to accurately classify LV function. Methods: Reference EF was obtained using conventional biplane measurements by experienced echocardiographers. In protocol 1, we used echocardiographic images from 166 clinical examinations. Both automated and reference EF values were used to categorize LV function as hyperdynamic (EF>73%), normal (53%–73%), mildly-to-moderately (30%–52%), or severely reduced ( Results: Protocol 1: the agreement with the reference EF values was good (intraclass correlation, 0.86–0.95), with biases Conclusions: The new machine-learning algorithm allows accurate automated evaluation of LV function from echocardiographic views commonly used in the POC setting. This approach will enable more POC personnel to accurately assess LV function.
- Published
- 2021