Deep Learning-Based Automated Echocardiographic Quantification of Left Ventricular Ejection Fraction: A Point-of-Care Solution.

Federico M Asch¹, Victor Mor-Avi², David Rubenson³, Steven Goldstein⁴, Muhamed Saric⁵, Issam Mikati⁶, Samuel Surette⁷, Ali Chaudhry⁷, Nicolas Poilvert⁷, Ha Hong⁷, Russ Horowitz⁶, Daniel Park⁸, Jose L Diaz-Gomez⁹, Brandon Boesch¹⁰, Sara Nikravan¹¹, Rachel B Liu¹², Carolyn Philips⁴, James D Thomas⁶, Randolph P Martin^7,13, Roberto M Lang²

¹MedStar Health Research Institute, Washington, DC (F.M.A.).

²University of Chicago, IL (V.M.-A., R.M.L.).

³Scripps Clinic and Prebys Cardiovascular Institute, La Jolla, CA (D.R.).

⁴MedStar Washington Hospital Center, DC (S.G., C.P.).

⁵New York University Langone Health (M.S.).

⁶Feinberg School of Medicine, Northwestern University, Chicago, IL (I.M., R.H., J.D.T.).

⁷Caption Health Inc, San Francisco, CA (S.S., A.C., N.P., H.H., R.P.M.).

⁸University of North Carolina Medical Center (D.P).

⁹Baylor St. Luke's Medical Center, Houston, TX (J.L.D.-G.).

¹⁰Highland Hospital, Oakland, CA (B.B.).

¹¹University of Washington Medical Center, Seattle (S.N.).

¹²Yale School of Medicine, New Haven, CT (R.B.L.).

¹³Emory University Medical Center, Atlanta, GA (R.P.M.).

PubMed DOI

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 an…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.