Deep learning for electrocardiogram interpretation: Bench to bedside.

Area of Science:

• Artificial intelligence
• Deep learning
• Cardiovascular medicine

Background:

• Deep learning (DL) advances AI for medical data analysis, aiding disease recognition and prediction.
• Electrocardiograms (ECGs) are crucial for diagnosing and monitoring heart conditions.

Purpose of the Study:

• Review DL applications in 12-lead ECG analysis.
• Assess the clinical effectiveness of DL tools through randomized controlled trials.
• Identify barriers to widespread clinical implementation of DL in ECG analysis.

Main Methods:

• Narrative review of DL applications in 12-lead ECG.
• Review of randomized controlled trials on DL tool effectiveness.
• Analysis of implementation barriers: regulatory, transparency, data privacy.

Main Results:

• DL algorithms demonstrate physician-comparable performance in detecting ECG abnormalities and heart diseases.
• Limited integration of DL-based ECG analysis into routine clinical practice despite promising developments.

Conclusions:

• DL holds potential to transform ECG analysis and enhance cardiovascular care.
• Addressing regulatory, transparency, and data privacy concerns is crucial for widespread clinical adoption.