arXiv:2607.01039v1 Announce Type: cross Abstract: Therapy-induced cardiotoxicity is the leading non-oncological cause of treatment interruption in breast cancer patients, yet early, automated risk stratification from routine cardiac imaging remains an unsolved problem. We present EchoRisk, the first curated, multicentre, longitudinal echocardiography dataset with explicit cardiotoxicity labels, released as the primary technical reference for the EchoRisk-MICCAI 2026 challenge. The dataset comprises 422 patients enrolled in the EU-funded CARDIOCARE prospective study across five European sites,
The increasing prevalence of AI in medical imaging and the rising focus on personalized patient care are driving the development of specialized datasets like EchoRisk.
This dataset provides a critical foundation for developing automated AI tools for early cardiotoxicity detection, significantly improving cancer treatment outcomes and reducing side effects.
The availability of a curated, multicentre, longitudinal dataset with explicit cardiotoxicity labels accelerates the development and validation of AI models in cardio-oncology.
- · AI in healthcare companies
- · Cardio-oncology researchers
- · Cancer patients
- · Medical imaging diagnostics
- · Traditional manual image analysis methods
Improved early detection of cardiotoxicity leads to more timely interventions for cancer patients.
The widespread adoption of AI-powered echocardiography analysis could standardize cardiac monitoring protocols globally.
Reduced cardiotoxicity burden could allow for more aggressive or prolonged cancer treatments, improving overall survival rates.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI