arXiv:2607. 16323v1 Announce Type: cross Abstract: Electrocardiography (ECG) is an inexpensive, standard-of-care test for cardiac symptoms, but front-line triage often lacks immediate access to definitive imaging such as echocardiography (ECHO) or cardiac magnetic resonance (CMR).
By Alexander Selivanov, Friederike Jungmann, Jan Kehrer, Karl-Ludwig Laugwitz, Eimo Martens, Daniel Rueckert
arXiv:2606. 02605v1 Announce Type: cross Abstract: Coronary artery stenosis is a common cardiovascular disease, with severe, untreated cases posing significant risks of heart attack.
By Nikola Cenikj, \"Ozg\"un Turgut, Alexander M\"uller, Alexander Steger, Jan Kehrer, Marcus Brugger, Daniel Rueckert, Philip M\"uller
arXiv:2607. 24035v1 Announce Type: cross Abstract: Explainable AI (XAI) is used to assess whether artificial intelligence models rely on meaningful patterns, yet explanations that appear plausible for individual predictions may systematically misrepresent model behavior.
By Nils Gumpfer, Michael Guckert, Samuel Sossalla, Birgit A{\ss}mus, Jennifer Hannig
arXiv:2608. 09053v1 Announce Type: cross Abstract: Cardiologists interpret electrocardiograms by localizing waveform components, measuring rhythm and interval patterns, and translating these structured observations into diagnostic evidence.
By Hongxiang Gao, He-yang Xu, Yuwen Li, Minghui Zhao, Zhipeng Cai, Xingyao Wang, Chenxi Yang, Jianqing Li, Chengyu Liu
arXiv:2609.21755v1 Announce Type: new
Abstract: Emergency department (ED) decision-making relies on heterogeneous clinical information, including patient history, vital signs, laboratory results, and...
By Jinning Liang, Mingcheng Zhu, Tingting Zhu
A multicenter study developed an AI-enabled electrocardiography (AI-ECG) model that predicts vessel-specific hemodynamically significant stenosis using coronary computed tomographic angiography (CCTA) as the reference. The model demonstrated strong discrimination in internal and external cohorts, including normal ECGs, and produced low-, intermediate-, and high-risk strata that correlated with stenosis severity and major adverse cardiovascular events. Calibration, decision curve analyses, and integration with guideline-based pre-test probability showed clinical utility, while waveform and attribution analyses revealed physiologically meaningful ECG features linked to high-risk predictions.
By Yujie Xiao, Qinghao Zhao, Gongzheng Tang, Hao Zhang, Zhuoran Kan, Deyun Zhang, Jun Li, Guangkun Nie, Xiaocheng Fang, Haoyu Wang, Shun Huang, Tong Liu, Jian Liu, Kangyin Chen, Shenda Hong