Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network

• URL: http://arxiv.org/abs/2504.01025v1
• Date: Fri, 28 Mar 2025 01:14:17 GMT
• Title: Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network
• Authors: Fubao Zhu, Yang Zhang, Gengmin Liang, Jiaofen Nan, Yanting Li, Chuang Han, Danyang Sun, Zhiguo Wang, Chen Zhao, Wenxuan Zhou, Jian He, Yi Xu, Iokfai Cheang, Xu Zhu, Yanli Zhou, Weihua Zhou,
• Abstract summary: This study develops and validates a deep learning-based diagnostic model for pulmonary hypertension (PH)<n>It is designed to classify patients as non-PH, pre-capillary PH, or post-capillary PH.<n>It has the potential to support clinical decision-making by effectively integrating multimodal data.
• Score: 32.50971951245164
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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