Deep learning-based diagnosis of aortic dissection using an electrocardiogram: Development, validation, and clinical implications of the AADE score

BACKGROUND: Aortic dissection (AD) is frequently associated with abnormalities in electrocardiographic findings. Advancements in medical technology present an opportunity to leverage these observations to improve patient diagnosis and care. OBJECTIVES: This study aimed to develop a deep learning artificial intelligence (AI) model for AD detection using electrocardiograms (ECGs) and introduce the AI-Aortic-Dissection-ECG (AADE) score to provide clinicians with a measure to determine AD severity. METHODS: From a cohort of 1878 patients, including 313 with AD, and 313 with chest pain (control group), we created training and validation subsets (7:3 ratio). A convolutional neural networks (CNN) model was trained for AD detection, with performance metrics like accuracy and F1 score (the harmonic mean of precision and recall) monitored. The AI-derived AADE score (0-1) was investigated against clinical parameters and ECG features over a median follow-up of 21.2 months. RESULTS: The CNN model demonstrated robust performance with an accuracy of 0.93 and an F1 score of 0.93 for the AD group, and an accuracy of 0.871 with an F1 score of 0.867 for the chest pain group. The AADE score showed correlations with specific ECG patterns and demonstrated that higher scores aligned with increased mortality risk. CONCLUSIONS: Our CNN-based AI model offers a promising approach for AD detection using ECG. The AADE score, based on AI, can serve as a pivotal tool in refining clinical assessments and management strategies.