用多模态数据融合模型区分肺动脉高压类型，辅助医生精准诊断。
Diagnosis of Pulmonary Hypertension by Integrating Multimodal Data with a Hybrid Graph Convolutional and Transformer Network
- 结合图卷积、卷积神经网络与Transformer处理心脏影像和临床数据。
- 在186例训练、18例测试数据上达到AUC 0.81、准确率73%。
- 对不同类型的肺动脉高压均有良好区分能力，适合临床辅助诊断使用。
早期准确诊断肺动脉高压（PH）对患者管理至关重要。区分前毛细血管性与后毛细血管性PH对治疗决策尤为关键。本研究构建并验证了一种基于深度学习的PH诊断模型，可将患者分类为非PH、前毛细血管性PH或后毛细血管性PH。回顾性分析南京医科大学第一附属医院204例患者数据（前毛细血管性PH 112例，后毛细血管性PH 32例，非PH对照组60例），诊断均通过右心导管术确认。每类选取6例作为测试集（共18例，占比10%），其余186例用于训练，该过程重复35次。提出一种融合图卷积网络（GCN）、卷积神经网络（CNN）与Transformer的模型，处理短轴（SAX）序列、四腔（4CH）序列及临床参数等多模态数据。测试集表现：AUC = 0.81 ± 0.06，准确率（ACC）= 0.73 ± 0.06。各类判别能力分别为：非PH（AUC = 0.74 ± 0.11）、前毛细血管性PH（AUC = 0.86 ± 0.06）、后毛细血管性PH（AUC = 0.83 ± 0.10）。该模型具备辅助临床决策潜力，可有效整合多模态数据以支持精准及时的诊断。
原文摘要 · Abstract (English)
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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