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. 2024 Jan 12:11:1330685.
doi: 10.3389/fcvm.2024.1330685. eCollection 2024.

Explanatory deep learning to predict elevated pulmonary artery pressure in children with ventricular septal defects using standard chest x-rays: a novel approach

Zhixin Li #  1 , Gang Luo #  1 , Zhixian Ji  1 , Sibao Wang  1 , Silin Pan  1
Affiliations

Explanatory deep learning to predict elevated pulmonary artery pressure in children with ventricular septal defects using standard chest x-rays: a novel approach

Zhixin Li et al. Front Cardiovasc Med. .

Abstract

Objective: Early risk assessment of pulmonary arterial hypertension (PAH) in patients with congenital heart disease (CHD) is crucial to ensure timely treatment. We hypothesize that applying artificial intelligence (AI) to chest x-rays (CXRs) could identify the future risk of PAH in patients with ventricular septal defect (VSD).

Methods: A total of 831 VSD patients (161 PAH-VSD, 670 nonPAH-VSD) was retrospectively included. A residual neural networks (ResNet) was trained for classify VSD patients with different outcomes based on chest radiographs. The endpoint of this study was the occurrence of PAH in VSD children before or after surgery.

Results: In the validation set, the AI algorithm achieved an area under the curve (AUC) of 0.82. In an independent test set, the AI algorithm significantly outperformed human observers in terms of AUC (0.81 vs. 0.65). Class Activation Mapping (CAM) images demonstrated the model's attention focused on the pulmonary artery segment.

Conclusion: The preliminary findings of this study suggest that the application of artificial intelligence to chest x-rays in VSD patients can effectively identify the risk of PAH.

Keywords: artificial intelligence; chest x-ray; deep learning—artificial intelligence; pulmonary arterial hypertension; ventricular septal defect.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of this study.
Figure 2
Figure 2
Validation group ROC curve of ResNet50 model.
Figure 3
Figure 3
(A) ResNet50 model external test group ROC curve. (B) ResNet50 model human observers assess risk ROC curve.
Figure 4
Figure 4
Resnet50 model external test group confusion matrix.
Figure 5
Figure 5
(A) Attention map of chest x-ray model for nonPAH-VSD patients. (B) Attention map of chest x-ray model for PAH-VSD patients.
See this image and copyright information in PMC

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The author(s) declare that no financial support was received for the research, authorship, and/or publication of this article.

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