Non-invasive estimation of haemodynamic parameters in pulmonary hypertension — A deep learning approach integrating all B-mode cine loops in an echocardiographic exam

Li-Hsin Cheng, Samer Alabed, Athanasios Charalampopoulos, Ze Ming Goh, Abdul Hameed, Eduard Holman, David G. Kiely, Mahan Salehi, Andrew J. Swift, Rob J. van der Geest
Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, PMLR 315:2818-2833, 2026.

Abstract

Pulmonary hypertension (PH) is heterogeneous with treatment strategy dependent on the underlying cause and disease severity. Haemodynamic parameters measured through right heart catheterization (RHC) is the gold standard for such diagnosis and desicion making. However, the invasive procedure is associated with a certain level of risk and is not suitable for every patient. Therefore, we seek to investigate whether haemodynamic parameters can be estimated non-invasively using a deep learning approach. The study is based on a retrospective analysis of 833 subjects with suspected PH identified from the ASPIRE research database. Convolutional neural networks were built to integrate B-mode echocardiographic cine loops from multiple views to predict key haemodynamic parameters. The model was able to integrate an arbitrary number of cine loops in the entire exam, unannotated with view names. Additionally, attention weights in feature fusion identify relevant and irrelevant cine loops to the model. The model-predicted mean pulmonary artery pressure (mPAP) correlated to the RHC-ground truth with a Pearson Correlation Coefficient (PCC) of 0.70. Attention weights indicated the apical 4-chamber (A4C) view to be especially relevant for mPAP prediction. Our results demonstrate the feasibility of estimating haemodynamic parameters non-invasively through deep learning models, integrating all B-mode cine loops of a cardiac ultrasound exam, achieving a moderate correlation to RHC measurements.

Cite this Paper

BibTeX
@InProceedings{pmlr-v315-cheng26a, title = {Non-invasive estimation of haemodynamic parameters in pulmonary hypertension — A deep learning approach integrating all B-mode cine loops in an echocardiographic exam}, author = {Cheng, Li-Hsin and Alabed, Samer and Charalampopoulos, Athanasios and Goh, Ze Ming and Hameed, Abdul and Holman, Eduard and Kiely, David G. and Salehi, Mahan and Swift, Andrew J. and van der Geest, Rob J.}, booktitle = {Proceedings of The 9th International Conference on Medical Imaging with Deep Learning}, pages = {2818--2833}, year = {2026}, editor = {Huo, Yuankai and Gao, Mingchen and Kuo, Chang-Fu and Jin, Yueming and Deng, Ruining}, volume = {315}, series = {Proceedings of Machine Learning Research}, month = {08--10 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v315/main/assets/cheng26a/cheng26a.pdf}, url = {https://proceedings.mlr.press/v315/cheng26a.html}, abstract = {Pulmonary hypertension (PH) is heterogeneous with treatment strategy dependent on the underlying cause and disease severity. Haemodynamic parameters measured through right heart catheterization (RHC) is the gold standard for such diagnosis and desicion making. However, the invasive procedure is associated with a certain level of risk and is not suitable for every patient. Therefore, we seek to investigate whether haemodynamic parameters can be estimated non-invasively using a deep learning approach. The study is based on a retrospective analysis of 833 subjects with suspected PH identified from the ASPIRE research database. Convolutional neural networks were built to integrate B-mode echocardiographic cine loops from multiple views to predict key haemodynamic parameters. The model was able to integrate an arbitrary number of cine loops in the entire exam, unannotated with view names. Additionally, attention weights in feature fusion identify relevant and irrelevant cine loops to the model. The model-predicted mean pulmonary artery pressure (mPAP) correlated to the RHC-ground truth with a Pearson Correlation Coefficient (PCC) of 0.70. Attention weights indicated the apical 4-chamber (A4C) view to be especially relevant for mPAP prediction. Our results demonstrate the feasibility of estimating haemodynamic parameters non-invasively through deep learning models, integrating all B-mode cine loops of a cardiac ultrasound exam, achieving a moderate correlation to RHC measurements.} }
Endnote
%0 Conference Paper %T Non-invasive estimation of haemodynamic parameters in pulmonary hypertension — A deep learning approach integrating all B-mode cine loops in an echocardiographic exam %A Li-Hsin Cheng %A Samer Alabed %A Athanasios Charalampopoulos %A Ze Ming Goh %A Abdul Hameed %A Eduard Holman %A David G. Kiely %A Mahan Salehi %A Andrew J. Swift %A Rob J. van der Geest %B Proceedings of The 9th International Conference on Medical Imaging with Deep Learning %C Proceedings of Machine Learning Research %D 2026 %E Yuankai Huo %E Mingchen Gao %E Chang-Fu Kuo %E Yueming Jin %E Ruining Deng %F pmlr-v315-cheng26a %I PMLR %P 2818--2833 %U https://proceedings.mlr.press/v315/cheng26a.html %V 315 %X Pulmonary hypertension (PH) is heterogeneous with treatment strategy dependent on the underlying cause and disease severity. Haemodynamic parameters measured through right heart catheterization (RHC) is the gold standard for such diagnosis and desicion making. However, the invasive procedure is associated with a certain level of risk and is not suitable for every patient. Therefore, we seek to investigate whether haemodynamic parameters can be estimated non-invasively using a deep learning approach. The study is based on a retrospective analysis of 833 subjects with suspected PH identified from the ASPIRE research database. Convolutional neural networks were built to integrate B-mode echocardiographic cine loops from multiple views to predict key haemodynamic parameters. The model was able to integrate an arbitrary number of cine loops in the entire exam, unannotated with view names. Additionally, attention weights in feature fusion identify relevant and irrelevant cine loops to the model. The model-predicted mean pulmonary artery pressure (mPAP) correlated to the RHC-ground truth with a Pearson Correlation Coefficient (PCC) of 0.70. Attention weights indicated the apical 4-chamber (A4C) view to be especially relevant for mPAP prediction. Our results demonstrate the feasibility of estimating haemodynamic parameters non-invasively through deep learning models, integrating all B-mode cine loops of a cardiac ultrasound exam, achieving a moderate correlation to RHC measurements.
APA
Cheng, L., Alabed, S., Charalampopoulos, A., Goh, Z.M., Hameed, A., Holman, E., Kiely, D.G., Salehi, M., Swift, A.J. & van der Geest, R.J.. (2026). Non-invasive estimation of haemodynamic parameters in pulmonary hypertension — A deep learning approach integrating all B-mode cine loops in an echocardiographic exam. Proceedings of The 9th International Conference on Medical Imaging with Deep Learning, in Proceedings of Machine Learning Research 315:2818-2833 Available from https://proceedings.mlr.press/v315/cheng26a.html.

Related Material