Artificial Intelligence-Driven Integration of ECG and Molecular Biomarkers in Pulmonary Embolism

TL;DR

This paper explores using AI to combine ECG and molecular biomarkers for better detection and management of pulmonary embolism.

Contribution

The novelty lies in integrating ECG and molecular biomarkers with AI to improve diagnostic accuracy for pulmonary embolism.

Findings

AI models can detect subtle patterns in ECG and biomarker data missed by conventional methods.

Combining multimodal data may enhance diagnostic accuracy and risk assessment for PE.

Further validation in large clinical studies is needed to confirm effectiveness.

Abstract

Pulmonary embolism (PE) is a serious cardiovascular condition and the third leading cause of cardiovascular mortality worldwide. However, its clinical presentation is often non-specific, making timely detection challenging. Biomarkers are commonly used to support early diagnosis and risk stratification. Molecular biomarkers provide information related to coagulation, inflammation, and cardiac injury. Electrocardiography (ECG) reflects cardiac functional changes caused by right ventricular (RV) stress and dilation secondary to increased pulmonary vascular resistance. Individually, these biomarkers have limited diagnostic accuracy. A promising approach to improving PE management involves integrating multimodal clinical data using Artificial Intelligence (AI). AI-based models can detect subtle patterns in ECG signals and molecular biomarker profiles that may be missed by conventional analysis. Combining these data sources may enhance diagnostic accuracy, refine risk assessment, and support personalized treatment. Despite ongoing challenges, including data quality, interpretability, and ethical considerations, AI-driven integration of ECG and molecular biomarkers represents a significant step forward in PE diagnosis and management. Further validation in large, prospective clinical studies is required.