13  Cardiology and Cardiovascular Medicine

TipLearning Objectives

Cardiovascular disease remains the leading cause of death globally. AI applications in cardiology span from ECG interpretation to cardiac imaging analysis to heart failure prediction. This chapter examines evidence-based AI tools across cardiovascular care. You will learn to:

  • Evaluate AI systems for ECG interpretation and arrhythmia detection
  • Understand AI applications in echocardiography and cardiac MRI
  • Assess AI tools for heart failure and cardiovascular risk prediction
  • Navigate AI-assisted interventional cardiology planning
  • Recognize limitations and failure modes of cardiovascular AI
  • Apply evidence-based frameworks for cardiology AI adoption

Essential for cardiologists, cardiac surgeons, electrophysiologists, and cardiovascular care teams.

The Clinical Context: Cardiovascular AI leverages rich physiologic signals (ECG, echo, MRI) and extensive outcome data. Applications range from well-validated (ECG interpretation) to promising (echo automation) to controversial (deep phenotyping for risk prediction). The abundance of cardiovascular data makes cardiology ideal for AI, but clinical validation and equity remain critical challenges.

Key Applications:

13.0.1 1. ECG Interpretation AI

✅ Automated ECG Analysis: - FDA-cleared algorithms in most ECG machines (decades-old technology) - High accuracy for standard diagnoses (STEMI, AFib, LVH) - Published guidelines support use (kadish2001acc?)

✅ AI-Enabled Hidden Patterns: - Detect conditions not visible to human eye - Low ejection fraction from normal-appearing ECG (attia2019screening?) - Hyperkalemia prediction (galloway2019development?) - Published in Lancet and JACC (attia2019screening?; galloway2019development?)

✅ Verdict: Well-validated, widely deployed. Some novel applications (EF prediction) require prospective trials before routine use.

13.0.2 2. Cardiac Imaging AI

✅ Echocardiography Automation: - Automated EF calculation, chamber quantification - Valve analysis, diastolic function assessment - Reduces inter-observer variability (omar2023precision?) - Published in JASE (omar2023precision?)

⚠️ Cardiac MRI and CT: - Automated segmentation and function analysis - Coronary artery calcium scoring - Perfusion defect detection - Strong technical performance but validation ongoing

13.0.3 3. Heart Failure and Risk Prediction

⚠️ ML-Based HF Prediction: - Predict HF hospitalization, mortality - AUC 0.75-0.85 in validation studies (angraal2020machine?) - Published in Circulation (angraal2020machine?) - High false positive rates limit clinical utility

13.0.4 4. Wearable Device AI

✅ AFib Detection from Smartwatches: - Apple Watch, Fitbit, others FDA-cleared - High sensitivity but low PPV (many false positives) (Perez et al. 2019) - Published in NEJM (Perez et al. 2019)

⚠️ Verdict: Useful screening tool but creates clinical management challenges (asymptomatic AFib, paroxysmal episodes).

Clinical Bottom Line: Cardiology AI shows tremendous promise, with some applications ready for clinical use (ECG interpretation, echo automation) and others requiring further validation (complex risk prediction, wearable integration). Equity concerns persist—algorithms trained predominantly on certain populations may underperform in others.

13.1 References