Romero-Farina Guillermo, Aguadé-Bruix Santiago, Cooke C David, Garcia Ernest V
Nuclear Medicine DepartmentVall d'Hebron University Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain.
Cardiology Department, Vall d'Hebron University Hospital, Universitat Autònoma de Barcelona, Barcelona, Spain.
Eur J Nucl Med Mol Imaging. 2025 Aug 29. doi: 10.1007/s00259-025-07510-w.
Cardiovascular risk stratification is crucial, as it is a key predictor of morbidity and mortality. The development of multiparametric scores for coronary risk stratification, integrated with artificial intelligence (AI), is important because it facilitates assessment in clinical practice. Therefore, prognostic coronary risk scores that incorporate multiple clinical variables and cardiac imaging data are necessary and deserve greater attention, as they provide a more comprehensive and accurate evaluation of individual patient risk across various clinical scenarios. Additionally, they support clinicians in making better-informed decisions based on a comprehensive assessment. Importantly, the widespread clinical use of multiparametric risk scores should be enabled by implementing standardized computer interfaces that can exchange the relevant imaging and clinical data needed to calculate these scores. The ongoing AI revolution, which increasingly relies on digital demographic, clinical, and imaging data, is rapidly making the availability of such data a reality.
心血管风险分层至关重要，因为它是发病率和死亡率的关键预测指标。开发与人工智能（AI）相结合的用于冠状动脉风险分层的多参数评分很重要，因为它有助于临床实践中的评估。因此，纳入多个临床变量和心脏成像数据的预后冠状动脉风险评分是必要的，值得更多关注，因为它们能在各种临床场景中对个体患者风险提供更全面、准确的评估。此外，它们有助于临床医生在全面评估的基础上做出更明智的决策。重要的是，应通过实施标准化计算机接口来实现多参数风险评分的广泛临床应用，这些接口能够交换计算这些评分所需的相关成像和临床数据。正在进行的AI革命越来越依赖数字人口统计学、临床和成像数据，正迅速使此类数据的可得性成为现实。
