Despite advances in reperfusion therapy, ST-segment elevation myocardial infarction remains a leading cause of mortality in the general population. In this work, we present a multicenter multimodal dataset designed for AI-based 12-month prognostic stratification after STEMI. The cohort includes 822 patients with predischarge post-PCI (Percutaneous Coronary Intervention) 12-lead ECGs and comprehensive clinical, laboratory, echocardiographic, angiographic and pharmacological data, along with standardized acquisition and 12-month follow-up for major adverse cardiovascular events (MACE). Data were anonymized and harmonized, and a computer-vision pipeline has been designed to detect lead regions and extract analyzable signals.
Designing a Multimodal Dataset for AI-Based Prognosis in STEMI
Simone Bartucci;Edoardo De Rose;Alessandro Quarta;Rossella Quarta;Alessia Donata Camarda;Alberto Polimeni;Francesco Calimeri
2026-01-01
Abstract
Despite advances in reperfusion therapy, ST-segment elevation myocardial infarction remains a leading cause of mortality in the general population. In this work, we present a multicenter multimodal dataset designed for AI-based 12-month prognostic stratification after STEMI. The cohort includes 822 patients with predischarge post-PCI (Percutaneous Coronary Intervention) 12-lead ECGs and comprehensive clinical, laboratory, echocardiographic, angiographic and pharmacological data, along with standardized acquisition and 12-month follow-up for major adverse cardiovascular events (MACE). Data were anonymized and harmonized, and a computer-vision pipeline has been designed to detect lead regions and extract analyzable signals.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
