The goal of this prospective multicenter observational study is to learn whether an artificial intelligence model based on electrocardiograms (ECGs) can help diagnose acute type A aortic dissection (TAAD) in adults who come to the emergency department with chest pain or related symptoms. The main question it aims to answer is:

Can the AI-ECG model accurately distinguish TAAD from other causes of chest pain in a real-world emergency setting? Researchers will compare the AI model's ECG-based predictions with the final diagnosis confirmed by computed tomographic angiography (CTA), which is the reference standard. Participants will undergo routine emergency ECG testing and subsequent diagnostic evaluation as part of standard care. Clinical and ECG data will be collected from five tertiary hospitals, and the model's diagnostic performance will be assessed across centers.

Inclusion Criteria:

• Male or female emergency department patients aged 18-80 years;
• Clear presentation of chest pain or related chest/back pain;
• Completion of standard 12-lead electrocardiography (ECG) within 24 hours after onset of chest pain;
• ECG signal quality meeting the following criteria: QRS amplitude ≥ 0.1 mV and noise proportion < 20%;
• Availability of subsequent diagnostic workup confirming whether the patient had acute type A aortic dissection (TAAD) or another definitive diagnosis.

Exclusion Criteria:

• Poor-quality ECG recordings, defined as missing leads in ≥ 3 leads or severe baseline instability;
• Indeterminate final diagnosis;
• History of prior surgery involving the aortic valve, aortic root, or ascending aorta.

Adult male and female emergency department patients aged 18 to 80 years who present with clear chest pain or related chest/back pain symptoms at five tertiary hospitals, undergo standard 12-lead electrocardiography within 24 hours of symptom onset, and subsequently receive definitive diagnostic evaluation confirming acute type A aortic dissection or another final diagnosis.