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Deep Learning-Based Electrocardiogram Analysis for Pulmonary Embolism Diagnosis: A Systematic Review and
Chen-Shu Wu1, Chia-Hsin Liu2, Gen-Min Lin3
1Division of Cardiology, Department of Medicine.
Acta Cardiologica Sinica
|May 28, 2026
Summary
Artificial intelligence (AI) models using electrocardiography (ECG) show moderate accuracy for detecting pulmonary embolism (PE). Multimodal AI approaches integrating clinical data show improved diagnostic performance for PE detection.
Area of Science:
- Cardiology
- Medical Imaging
- Artificial Intelligence
Background:
- Computed tomography pulmonary angiography (CTPA) is the gold standard for pulmonary embolism (PE) diagnosis but has risks and delays.
- Electrocardiography (ECG) is accessible and safe but has low diagnostic sensitivity for PE.
Purpose of the Study:
- Evaluate the diagnostic performance of AI-based deep learning models using ECG for PE detection.
- Identify strengths, limitations, and future directions for AI in PE diagnosis.
Main Methods:
- Meta-analysis of studies using AI models with ECG for PE detection.
- CTPA was used as the reference standard in included studies.
- Searched PubMed, Embase, and Cochrane Library.
Main Results:
- Analyzed 5 studies with 467,017 patients.
- Pooled sensitivity: 70%, specificity: 67% for AI-ECG models.
- Area under the curve (AUC) of 0.754 indicated moderate accuracy; significant heterogeneity observed.
Conclusions:
- AI-ECG models have moderate accuracy for acute PE detection, showing potential for clinical screening.
- Multimodal AI models integrating clinical data outperformed ECG-only approaches.
- Need for standardized methods and larger studies due to heterogeneity and limited validation.
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