Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network
Journal:
arXiv
Published Date:
Mar 28, 2025
Abstract
Early and accurate diagnosis of pulmonary hypertension (PH) is essential for
optimal patient management. Differentiating between pre-capillary and
post-capillary PH is critical for guiding treatment decisions. This study
develops and validates a deep learning-based diagnostic model for PH, designed
to classify patients as non-PH, pre-capillary PH, or post-capillary PH. This
retrospective study analyzed data from 204 patients (112 with pre-capillary PH,
32 with post-capillary PH, and 60 non-PH controls) at the First Affiliated
Hospital of Nanjing Medical University. Diagnoses were confirmed through right
heart catheterization. We selected 6 samples from each category for the test
set (18 samples, 10%), with the remaining 186 samples used for the training
set. This process was repeated 35 times for testing. This paper proposes a deep
learning model that combines Graph convolutional networks (GCN), Convolutional
neural networks (CNN), and Transformers. The model was developed to process
multimodal data, including short-axis (SAX) sequences, four-chamber (4CH)
sequences, and clinical parameters. Our model achieved a performance of Area
under the receiver operating characteristic curve (AUC) = 0.81 +- 0.06(standard
deviation) and Accuracy (ACC) = 0.73 +- 0.06 on the test set. The
discriminative abilities were as follows: non-PH subjects (AUC = 0.74 +- 0.11),
pre-capillary PH (AUC = 0.86 +- 0.06), and post-capillary PH (AUC = 0.83 +-
0.10). It has the potential to support clinical decision-making by effectively
integrating multimodal data to assist physicians in making accurate and timely
diagnoses.
