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. 2025 Jun 2:16:1592593.
doi: 10.3389/fphys.2025.1592593. eCollection 2025.

A non-invasive prediction model for coronary artery stenosis severity based on multimodal data

Jiyu Zhang  1 , Jiatuo Xu  1 , Liping Tu  1 , Tao Jiang  1 , Yu Wang  1 , Jijie Xu  2
Affiliations

A non-invasive prediction model for coronary artery stenosis severity based on multimodal data

Jiyu Zhang et al. Front Physiol. .

Abstract

Introduction: Coronary artery disease (CAD) diagnosis currently relies on invasive coronary angiography for stenosis severity assessment, carrying inherent procedural risks. This study develops a transformer-based multimodal prediction model to provide a clinically reliable non-invasive alternative. By integrating heterogeneous biomarkers including facial morphometrics, cardiovascular waveforms and biochemical indicators, we aim to establish an interpretable framework for precision risk stratification.

Methods: The study utilized a transformer-based architecture integrated with residual modules and adaptive weighting mechanisms. Multimodal data, including facial features, lip and tongue images, pulse and pressure wave amplitudes, and laboratory indicators, were collected from 488 CAD patients. These data were processed and analyzed to predict the severity of coronary artery stenosis. The model's performance was evaluated using both internal and external validation datasets.

Results: The proposed model demonstrated high predictive accuracy, achieving over 90% accuracy in assessing coronary artery stenosis risk on the training dataset. External validation on real-world data further confirmed the model's robustness, with an accuracy of 85% on the validation set. The integration of multimodal data and advanced architectural components significantly enhanced the model's performance.

Conclusion: This study developed a non-invasive, transformer-based multimodal prediction model for assessing coronary artery stenosis severity. By combining diverse data sources and advanced machine learning techniques, the model offers a clinically viable alternative to invasive diagnostic methods. The results highlight the potential of multimodal data integration in improving CAD diagnosis and patient care.

Keywords: cardiovascular risk assessment; coronary artery disease; deep learning approaches; machine learning for disease risk stratification; multimodal prediction.

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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
Data collection flow chart.
FIGURE 2
FIGURE 2
Tongue diagnosis and face-to-face diagnosis collection equipment.
FIGURE 3
FIGURE 3
PDA-1 pulse equipment.
FIGURE 4
FIGURE 4
Adaptive weighted cardiovascular occlusion prediction model (AWCOP_Model).
FIGURE 5
FIGURE 5
Scale alignment and weight initialization for multi-modal data.
FIGURE 6
FIGURE 6
Dynamic weight adjustment and fusion for multi-modal data.
FIGURE 7
FIGURE 7
Grad-CAM heatmap generation for image and pulse pressure wave.
FIGURE 8
FIGURE 8
Top 15 clinical data importance.
FIGURE 9
FIGURE 9
(A) Different models of AUC; (B) Different models of ACC; (C) Different models of F1; (D) Different models of Recall.
FIGURE 10
FIGURE 10
Comparison of the training parameters for the different learning rates.
FIGURE 11
FIGURE 11
Pulse pressure-wave heat map.
FIGURE 12
FIGURE 12
Model of the complexion heat map.
FIGURE 13
FIGURE 13
Model tongue thermal map.
See this image and copyright information in PMC

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