@inbook{6378404f88554ae59f73efa637e1f30e,
title = "AI-Enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography",
abstract = "Left ventricular (LV) function is an important factor in terms of patient management, outcome, and long-term survival of patients with heart disease. The most recently published clinical guidelines for heart failure recognise that over reliance on only one measure of cardiac function (LV ejection fraction) as a diagnostic and treatment stratification biomarker is suboptimal. Recent advances in AI-based echocardiography analysis have shown excellent results on automated estimation of LV volumes and LV ejection fraction. However, from time-varying 2-D echocardiography acquisition, a richer description of cardiac function can be obtained by estimating functional biomarkers from the complete cardiac cycle. In this work we propose for the first time an AI approach for deriving advanced biomarkers of systolic and diastolic LV function from 2-D echocardiography based on segmentations of the full cardiac cycle. These biomarkers will allow clinicians to obtain a much richer picture of the heart in health and disease. The AI model is based on the {\textquoteright}nn-Unet{\textquoteright} framework and was trained and tested using four different databases. Results show excellent agreement between manual and automated analysis and showcase the potential of the advanced systolic and diastolic biomarkers for patient stratification. Finally, for a subset of 50 cases, we perform a correlation analysis between clinical biomarkers derived from echocardiography and cardiac magnetic resonance and we show a very strong relationship between the two modalities.",
keywords = "Cardiac function, Deep learning, Echocardiography, Image segmentation",
author = "Esther Puyol-Ant{\'o}n and Bram Ruijsink and Sidhu, {Baldeep S.} and Justin Gould and Bradley Porter and Elliott, {Mark K.} and Vishal Mehta and Haotian Gu and Rinaldi, {Christopher A.} and Martin cowie and Phil Chowienczyk and Reza Razavi and King, {Andrew P.}",
note = "Funding Information: Acknowledgements. This work was supported by the EPSRC (EP/R005516/1 and EP/P001009/1), the Wellcome EPSRC Centre for Medical Engineering at the School of Biomedical Engineering and Imaging Sciences, King{\textquoteright}s College London (WT 203148/Z/16/Z). The authors acknowledge financial support (support) the National Institute for Health Research (NIHR) Cardiovascular MedTech Co-operative award to the Guy{\textquoteright}s and St Thomas{\textquoteright} NHS Foundation Trust and the Department of Health National Institute for Health Research (NIHR) comprehensive Biomedical Research Centre award to Guy{\textquoteright}s & St Thomas{\textquoteright} NHS Foundation Trust in partnership with King{\textquoteright}s College London. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 3rd International Workshop of Advances in Simplifying Medical Ultrasound, ASMUS 2022, held in Conjunction with 25th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 18-09-2022",
year = "2022",
month = sep,
day = "15",
doi = "10.1007/978-3-031-16902-1_8",
language = "English",
isbn = "9783031169014",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "75--85",
editor = "Stephen Aylward and Noble, {J. Alison} and Yipeng Hu and Su-Lin Lee and Zachary Baum and Zhe Min",
booktitle = "Simplifying Medical Ultrasound - 3rd International Workshop, ASMUS 2022, held in Conjunction with MICCAI 2022, Proceedings",
address = "Germany",
}