Signal or noise? Evaluating commonly used attribution methods for explaining deep neural networks in electrocardiogram classification [0.03%]
Bauke K O Arends,Wouter A C van Amsterdam,Pim van der Harst et al.
Bauke K O Arends et al.
Aims: Attribution-based explainability methods are widely used in electrocardiogram (ECG) analysis to interpret predictions from 'black-box' deep neural networks (DNNs). To be useful in clinical applications, attribution ...
Fully automated, deep learning, cardiac CT-based multimodal network for cardiovascular risk stratification in high-risk perioperative patients [0.03%]
Juan Lu,Gavin Huangfu,Abdul Ihdayhid et al.
Juan Lu et al.
Aims: Major adverse cardiac events (MACE) significantly impact perioperative morbidity and mortality. We aimed to develop a fully automated multimodal deep learning (DL) system integrating patient demographics, comorbidit...
Artificial intelligence 12-lead electrocardiography to determine atrial fibrillation risk among UK Biobank participants with predisposing conditions [0.03%]
针对具有房颤诱发因素的英国生物样本库受试者使用人工智能十二导联心电图以确定其患房颤的风险
Yi Zheng,Konstantinos C Siontis,Zachi I Attia et al.
Yi Zheng et al.
Aims: Artificial intelligence electrocardiography (AI-ECG) algorithms are emerging tools for identifying individuals at risk of atrial fibrillation (AF). We evaluated the predictive performance of a validated AI-ECG algor...
Three-year risk prediction of aortic stenosis using routine medical records: derivation and validation in 919 954 individuals from two cohorts [0.03%]
基于常规医疗记录的主动脉瓣狭窄三年风险预测模型的构建及验证（来自两个队列共计919,954人）
Ben O Petrazzini,Waqas A Malick,Stamatios Lerakis et al.
Ben O Petrazzini et al.
Aims: All-cause mortality ranges between 33% and 42% for individuals with untreated moderate to severe aortic stenosis (AS). Transcatheter aortic valve replacement makes this a treatable condition, if identified early. Ma...
Conversational AI for remote monitoring in heart failure: a prospective controlled pilot study [0.03%]
心力衰竭远程监测的对话人工智能：一项前瞻性控制试点研究
Aleix Olivella,Ana B Méndez Fernández,Emmanuel Giménez García et al.
Aleix Olivella et al.
Aims: Heart failure (HF) requires scalable strategies to detect decompensation early and reduce hospitalizations. Existing telemonitoring tools are often invasive, complex, or poorly integrated into routine care. To evalu...
Artificial intelligence-powered automatic coronary computed tomography angiography plaque quantification: comparison against optical coherence tomography [0.03%]
基于人工智能的冠状动脉CT造影斑块量化分析：与光学相干断层扫描对比研究
Guanyu Li,Wei Yu,Zhiqing Wang et al.
Guanyu Li et al.
Aims: Coronary computed tomography angiography (CCTA) enables a non-invasive, comprehensive assessment of coronary artery disease, and artificial intelligence (AI) offers the potential to improve CCTA image interpretation...
Deep learning-based multi-view echocardiographic framework for comprehensive diagnosis of pericardial disease [0.03%]
基于深度学习的多视图心脏超声诊断心包疾病的框架
Sihyeon Jeong,In Tae Moon,Jaeik Jeon et al.
Sihyeon Jeong et al.
Aims: Pericardial disease spans a wide spectrum from small effusions to life-threatening tamponade or constriction. Transthoracic echocardiography (TTE) is the main diagnostic tool, but its interpretation is limited by op...
Association between electrocardiographic age and cognitive function: findings from the UK biobank and Framingham Heart Study [0.03%]
心电图老化与认知功能的关系：英国生物银行和弗明翰心脏研究的结果
Bernard Ofosuhene,Huitong Ding,Heaven Y Tatere et al.
Bernard Ofosuhene et al.
Aims: Biological age derived from 12-lead electrocardiograms (ECGs) using deep learning has emerged as a promising marker of physiological ageing. However, its relationship with cognitive performance remains poorly unders...
Total product lifecycle regulatory considerations and recommendations for generative AI-enabled medical devices [0.03%]
生成式人工智能驱动的医疗器械全生命周期监管考量及建议
Antonis A Armoundas,Jagmeet P Singh
Antonis A Armoundas
As generative artificial intelligence (GenAI) emerges within the healthcare ecosystem, the regulatory environment surrounding these technologies remains fragmented. Importantly, GenAI in healthcare requires adapting the established Total Pr...
Correction [0.03%]
改正通知书
[This corrects the article DOI: 10.1093/ehjdh/ztaf143.150.][This corrects the article DOI: 10.1093/ehjdh/ztaf143.151.][This corrects the article DOI: 10.1093/ehjdh/ztaf143.152.][This corrects the article DOI: 10.1093/ehjdh/ztaf143.153.][Thi...
Published Erratum
European heart journal. Digital health. 2026 Mar 2;7(2):ztag033. DOI:10.1093/ehjdh/ztag033 2026