Development of automated neural network prediction for echocardiographic left ventricular ejection fraction.
Zhang, Yuting Liu, Boyang Bunting, Karina V Brind, David Thorley, Alexander Karwath, Andreas Lu, Wenqi Zhou, Diwei Wang, Xiaoxia Mobley, Alastair R
Zhang, Yuting
Liu, Boyang
Bunting, Karina V
Brind, David
Thorley, Alexander
Karwath, Andreas
Lu, Wenqi
Zhou, Diwei
Wang, Xiaoxia
Mobley, Alastair R
Abstract
This method was developed and internally validated in an open-source dataset containing 10,030 echocardiograms. The Pearson's correlation coefficient was 0.83 for LVEF prediction compared to expert human analysis (p < 0.001), with a subsequent area under the receiver operator curve (AUROC) of 0.98 (95% confidence interval 0.97 to 0.99) for categorisation of HF with reduced ejection (HFrEF; LVEF<40%). In an external dataset with 200 echocardiograms, this method achieved an AUC of 0.90 (95% confidence interval 0.88 to 0.91) for HFrEF assessment.
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Date
2024-04-03
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Zhang Y, Liu B, Bunting KV, Brind D, Thorley A, Karwath A, Lu W, Zhou D, Wang X, Mobley AR, Tica O, Gkoutos GV, Kotecha D, Duan J. Development of automated neural network prediction for echocardiographic left ventricular ejection fraction. Front Med (Lausanne). 2024 Apr 3;11:1354070. doi: 10.3389/fmed.2024.1354070. PMID: 38686369; PMCID: PMC11057494.