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

Early and accurate diagnosis of pulmonary hypertension (PH), including differentiating pre-capillary from post-capillary PH, is crucial for guiding effective clinical management. This study developed and validated a deep learning-based diagnostic model to classify patients into non-PH, pre-capillary PH, or post-capillary PH categories. A retrospective dataset from 204 patients (112 pre-capillary PH, 32 post-capillary PH, and 60 non-PH controls) was collected at the First Affiliated Hospital of Nanjing Medical University, with diagnoses confirmed by right heart catheterization (RHC). Patients were randomly divided into training (186 patients, 90%) and testing sets (18 patients, 10%) stratified by diagnostic category. We trained and evaluated the model using 35 repeated random splits. The proposed deep learning model combined graph convolutional networks (GCN), convolutional neural networks (CNN), and Transformers to analyze multimodal data, including cine short-axis (SAX) sequences, four-chamber (4CH) sequences, and clinical parameters. Across test splits, the model achieved an overall area under the receiver operating characteristic curve (AUC) of 0.814 ± 0.06 and accuracy (ACC) of 0.734 ± 0.06 (mean ± SD). Class-specific AUCs were 0.745 ± 0.11 for non-PH, 0.863 ± 0.06 for pre-capillary PH, and 0.834 ± 0.10 for post-capillary PH, indicating good discriminative ability. This study demonstrated three-class PH classification using multimodal inputs. By fusing imaging and clinical data, the model may support accurate and timely clinical decision-making in PH.