Zhang, YutingLiu, BoyangBunting, Karina VBrind, DavidThorley, AlexanderKarwath, AndreasLu, WenqiZhou, DiweiWang, XiaoxiaMobley, Alastair RTica, OtiliaGkoutos, Georgios VKotecha, DipakDuan, Jinming2024-05-092024-05-092024-04-03Zhang 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: PMC11057494.2296-858X10.3389/fmed.2024.135407038686369http://hdl.handle.net/20.500.14200/4464This 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.enCopyright © 2024 Zhang, Liu, Bunting, Brind, Thorley, Karwath, Lu, Zhou, Wang, Mobley, Tica, Gkoutos, Kotecha and Duan.CardiologyArticle