Wolff-Parkinson-White Mimicking Posterior STEMI: When Even AI Gets It Wrong

Area of Science:

• Cardiology
• Medical Diagnostics
• Artificial Intelligence in Medicine

Background:

• Wolff-Parkinson-White (WPW) syndrome presents electrocardiogram (ECG) findings that can be mistaken for acute ischemic events.
• Misinterpretation of WPW on ECG can lead to incorrect diagnosis of acute coronary syndromes (ACS).

Purpose of the Study:

• To illustrate a case where WPW syndrome mimicked ischemic ECG patterns.
• To emphasize the necessity of a comprehensive diagnostic approach for differentiating WPW from myocardial ischemia.
• To highlight the role of AI in improving diagnostic accuracy.

Main Methods:

• Case report presentation.
• Review of ECG findings characteristic of WPW and ischemia.
• Integration of clinical assessment, echocardiography, and troponin levels.
• Application of AI-assisted ECG analysis for diagnostic support.

Main Results:

• The case demonstrated the potential for WPW to produce ECG patterns resembling ischemia.
• Combined diagnostic modalities successfully differentiated WPW pre-excitation from true ischemic changes.
• AI-assisted analysis aided in accurate interpretation of complex ECG data.

Conclusions:

• Accurate differentiation between WPW and myocardial ischemia is crucial for appropriate patient management.
• A multi-faceted diagnostic strategy, including AI, is essential to avoid misdiagnosis of ACS in patients with WPW.
• Timely and correct diagnosis prevents unnecessary and potentially harmful interventions.