Mean pulmonary artery pressure prediction with explainable multi-view cardiac MR cine series deep learning model.

Autor:	Cheng LH; Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center, the Netherlands., Sun X; Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center, the Netherlands., Elliot C; Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, UK., Condliffe R; Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, UK., Kiely DG; Sheffield Pulmonary Vascular Disease Unit, Sheffield Teaching Hospitals NHS Foundation Trust, UK., Alabed S; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK., Swift AJ; Department of Infection, Immunity and Cardiovascular Disease, University of Sheffield, UK., van der Geest RJ; Division of Image Processing (LKEB), Department of Radiology, Leiden University Medical Center, the Netherlands. Electronic address: r.j.van_der_geest@lumc.nl.
Jazyk:	angličtina
Zdroj:	Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance [J Cardiovasc Magn Reson] 2024 Dec 05, pp. 101133. Date of Electronic Publication: 2024 Dec 05.
DOI:	10.1016/j.jocmr.2024.101133
Abstrakt:	Background: Pulmonary hypertension (PH) is a heterogeneous condition and regardless of aetiology impacts negatively on survival. Diagnosis of PH is based on hemodynamic parameters measured invasively at right heart catheterization (RHC), however, a non-invasive alternative would be clinically valuable. Our aim was to estimate RHC parameters non-invasively from cardiac MR data using deep learning models and to identify key contributing imaging features. Methods: We constructed an explainable convolutional neural network (CNN) taking cardiac MR cine series from 4 different views as input to predict mean pulmonary artery pressure (mPAP). The model was trained and evaluated on 1646 examinations. The model's attention weight and predictive performance associated with each frame, view, or phase was used to judge its importance. Additionally, the importance of each cardiac chamber was inferred by perturbing part of the input pixels. Results: The model achieved a Pearson Correlation Coefficient (PCC) of 0.80 and R 2 of 0.64 in predicting mPAP, and identified the right ventricle (RV) region on short-axis (SAX) view to be especially informative. Conclusions: Hemodynamic parameters can be estimated non-invasively with a CNN, using MR cine series from 4 views, revealing key contributing features at the same time. Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Competing interests The authors have no competing interests to declare. (Copyright © 2024. Published by Elsevier Inc.)
Databáze:	MEDLINE
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