Deep learning for automated left ventricular outflow tract diameter measurements in 2D echocardiography

Cardiovascular Ultrasound

Research

Andrew Gilbert¹  Bjørn-Jostein Singstad²  Lars Gunnar Klæboe²  Pål Haugar Brekke²  Magnus Rogstadkjernet³  Sigurd Zijun Zha³  Eigil Samset⁴  Helge Skulstad⁵  Thor Edvardsen⁵ 

[1] GE HealthCare, Oslo, Norway;Oslo University Hospital, Rikshospitalet, Oslo, Norway;University of Oslo, Oslo, Norway;University of Oslo, Oslo, Norway;GE HealthCare, Oslo, Norway;University of Oslo, Oslo, Norway;Oslo University Hospital, Rikshospitalet, Oslo, Norway;

关键词: Deep learning;    Left ventricular outflow tract;    Transthoracic echocardiography;    Machine learning;    Automated measurements;   

DOI  :  10.1186/s12947-023-00317-5

 received in 2023-07-16, accepted in 2023-10-02,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundMeasurement of the left ventricular outflow tract diameter (LVOTd) in echocardiography is a common source of error when used to calculate the stroke volume. The aim of this study is to assess whether a deep learning (DL) model, trained on a clinical echocardiographic dataset, can perform automatic LVOTd measurements on par with expert cardiologists.MethodsData consisted of 649 consecutive transthoracic echocardiographic examinations of patients with coronary artery disease admitted to a university hospital. 1304 LVOTd measurements in the parasternal long axis (PLAX) and zoomed parasternal long axis views (ZPLAX) were collected, with each patient having 1–6 measurements per examination. Data quality control was performed by an expert cardiologist, and spatial geometry data was preserved for each LVOTd measurement to convert DL predictions into metric units. A convolutional neural network based on the U-Net was used as the DL model.ResultsThe mean absolute LVOTd error was 1.04 (95% confidence interval [CI] 0.90–1.19) mm for DL predictions on the test set. The mean relative LVOTd errors across all data subgroups ranged from 3.8 to 5.1% for the test set. Generally, the DL model had superior performance on the ZPLAX view compared to the PLAX view. DL model precision for patients with repeated LVOTd measurements had a mean coefficient of variation of 2.2 (95% CI 1.6–2.7) %, which was comparable to the clinicians for the test set.ConclusionDL for automatic LVOTd measurements in PLAX and ZPLAX views is feasible when trained on a limited clinical dataset. While the DL predicted LVOTd measurements were within the expected range of clinical inter-observer variability, the robustness of the DL model requires validation on independent datasets. Future experiments using temporal information and anatomical constraints could improve valvular identification and reduce outliers, which are challenges that must be addressed before clinical utilization.Graphical Abstract

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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