Keynote Lecture

Cancer survival continues to improve, shifting the focus toward the prevention and early detection of cardiovascular complications associated with modern cancer therapies. Echocardiography remains the cornerstone of cardiovascular surveillance throughout the cancer journey. Nevertheless, important challenges persist regarding the timely identification of subclinical cardiac dysfunction, the reproducibility of imaging biomarkers, and their integration into routine clinical decision making.

This keynote provides a comprehensive clinical overview of contemporary echocardiographic surveillance in cardio-oncology, covering both established and emerging imaging biomarkers for cancer therapy-related cardiac dysfunction. The presentation discusses the evolving role of left ventricular ejection fraction, myocardial deformation imaging, and multimodal assessment within current international recommendations, while addressing practical limitations encountered in everyday clinical practice.

A major focus is the rapidly expanding role of artificial intelligence in cardiovascular imaging. Advances in automated image analysis, foundation models, and predictive machine learning are creating new opportunities for earlier detection of cardiotoxicity, personalised risk prediction, and scalable clinical implementation. The keynote demonstrates how clinically driven AI development, supported by initiatives such as the EchoRisk-MICCAI Challenge, has the potential to transform cardiovascular surveillance from reactive diagnosis to proactive risk prediction.

The lecture concludes by outlining future directions for AI-enabled precision cardio-oncology and discussing the importance of close collaboration between clinicians, engineers, and computer scientists in developing robust, interpretable, and clinically deployable solutions.

• 01
  Understand the current challenges in cardiovascular surveillance during cancer treatment.
• 02
  Review contemporary echocardiographic biomarkers for early detection of cancer therapy-related cardiac dysfunction.
• 03
  Explore recent advances in artificial intelligence, foundation models, and machine learning for cardiovascular imaging.
• 04
  Appreciate the importance of clinically meaningful AI evaluation through benchmark challenges such as EchoRisk-MICCAI.
• 05
  Discuss future directions for precision cardio-oncology driven by artificial intelligence.