Development of automated neural network prediction for echocardiographic left ventricular ejection fraction.
Author
Zhang, YutingLiu, Boyang
Bunting, Karina V
Brind, David
Thorley, Alexander
Karwath, Andreas
Lu, Wenqi
Zhou, Diwei
Wang, Xiaoxia
Mobley, Alastair R
Tica, Otilia
Gkoutos, Georgios V
Kotecha, Dipak
Duan, Jinming
Publication date
2024-04-03Subject
Cardiology
Metadata
Show full item recordAbstract
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.Citation
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.Type
ArticlePMID
38686369Journal
Frontiers in MedicinePublisher
Frontiers Mediaae974a485f413a2113503eed53cd6c53
10.3389/fmed.2024.1354070