Cross-Modal Contrastive Learning of ECG and Angiography Representations for Severe Stenosis Classification
Chair for AI in Healthcare and Medicine, Technical University of Munich and TUM University Hospital（人工智能在医疗与医学中的研究所，慕尼黑技术大学及慕尼黑大学医院）；Department of Computing, Imperial College London（伦敦帝国理工学院计算机系）；Munich Center for Machine Learning (MCML), Munich, Germany（慕尼黑机器学习中心（MCML））；Department of Internal Medicine, TUM University Hospital（慕尼黑大学医院内科学系）
Nikola Cenikj, Özgün Turgut, Alexander Müller, Alexander Steger, Jan Kehrer, Marcus Brugger, Daniel Rueckert, Philip Müller
Coronary artery stenosis is a common cardiovascular disease, with severe, untreated cases posing significant risks of heart attack. Although coronary (X-ray) angiograms remain the standard for stenosis diagnosis, they are invasive, time- and resource-intensive, and therefore only performed on patients with a high probability of disease based on symptoms and prior clinical tests. However, a subset of patients, especially those without symptoms, may remain undiagnosed. Detecting indications of stenosis from ECGs, which are fast, cheap, non-invasive, and thus routinely acquired even in asymptomatic patients, would support early diagnosis. However, as no reliable stenosis-specific signal has been identified in ECGs, they can not currently be used for stenosis risk stratification. To address this, we introduce StenCE, a pretraining framework, allowing stratification of patients based on features derived directly from ECGs. Evaluations across varying stenosis severity thresholds and additional ECG disease classification tasks demonstrate consistent performance improvements across different ECG encoders, outperforming previous work. The obtained models successfully detect signals for stenosis diagnosis in ECGs and are the first to achieve high performance in severe stenosis classification. The source code is available at https://github.com/NikolaCenic/ecg-stenosis-cls.
Coronary heart disease (CHD) is the leading cause of adult death in the United States and worldwide, and for which the coronary angiography procedure is the primary gateway for diagnosis and clinical management decisions. The standard-of-care for interpretation of coronary angiograms depends upon ad-hoc visual assessment by the physician operator. However, ad-hoc visual interpretation of angiograms is poorly reproducible, highly variable and bias prone. Here we show for the first time that fully-automated angiogram interpretation to estimate coronary artery stenosis is possible using a sequenc…
Instituto Superior Técnico（高等技术学院）；University of Lisbon（里斯本大学）；CCUL（里斯本大学中心学院）；Lisbon School of Medicine（里斯本医学院）
Coronary artery disease leading up to stenosis, the partial or total blocking of coronary arteries, is a severe condition that affects millions of patients each year. Automated identification and classification of stenosis severity from minimally invasive procedures would be of great clinical value, but existing methods do not match the accuracy of experienced cardiologists, due to the complexity of the task. Although a number of computational approaches for quantitative assessment of stenosis have been proposed to date, the performance of these methods is still far from the required levels fo…
A Deep Learning Model for Coronary Artery Segmentation and Quantitative Stenosis Detection in Angiographic Images
School of Mathematical Sciences, Ocean University of China（中国海洋大学数学科学学院）；School of Haide, Ocean University of China（中国海洋大学海德学院）；School of Ocean and Atmosphere, Ocean University of China（中国海洋大学海洋与大气学院）；Qilu Hospital (Qingdao), Cheeloo College of Medicine, Shandong University（山东大学齐鲁医院（青岛））
A Federated Learning Framework for Stenosis Detection
Università Politecnica delle Marche（马尔凯理工大学）；Università degli Studi di Macerata（马切拉塔大学）；Azienda Ospedaliero Universitaria delle Marche（马尔凯大学医院）；Ospedale del Mare, ASL NA 1（ASL NA 1 海洋医院）；University of Macerata（马切拉塔大学）；Scuola Superiore Sant’Anna（圣安娜高等研究学院）
Deep denoising autoencoder-based non-invasive blood flow detection for arteriovenous fistula
Research Center for Information Technology Innovation, Academia Sinica（中央研究院资讯科技创新研究中心）；National Taiwan University（国立台湾大学）；Center for Geriatrics and Gerontology, Taipei Veterans General Hospital（台北荣民总医院老年医学与老年学中心）；Center for Healthy Longevity and Aging Sciences, National Yang Ming Chiao Tung University（国立阳明交通大学健康长寿与老化科学中心）；Kai-Yan Clinic（凯燕诊所）；School of Medicine, National Yang Ming Chiao Tung University（国立阳明交通大学医学院）；Institute of Clinical Medicine, National Yang Ming Chiao Tung University（国立阳明交通大学临床医学研究所）；Division of Cardiology, Department of Medicine, Taipei Veterans General Hospital（台北荣民总医院内科部心脏科）；Cardiovascular Research Center, Taipei Veterans General Hospital（台北荣民总医院心血管研究中心）
Biomedical Imaging Research Institute, Cedars Sinai Medical Center（雪松西奈医疗中心生物医学影像研究所）；Peking University（北京大学）；University of California, Los Angeles（加州大学洛杉矶分校）；Yonsei University College of Medicine（延世大学医学院）
Coronary calcium causes beam hardening and blooming artifacts on cardiac computed tomography angiography (CTA) images, which lead to overestimation of lumen stenosis and reduction of diagnostic specificity. To properly remove coronary calcification and restore arterial lumen precisely, we propose a machine learning-based method with a multi-step inpainting process. We developed a new network configuration, Dense-Unet, to achieve optimal performance with low computational cost. Results after the calcium removal process were validated by comparing with gold-standard X-ray angiography. Our result…
ImageCAS: A Large-Scale Dataset and Benchmark for Coronary Artery Segmentation based on Computed Tomography Angiography Images
Guangdong University of Technology（广东工业大学）；Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences)（广东省人民医院（广东省医学科学院））；Southern Medical University（南方医科大学）；Missouri University of Science and Technology（密苏里科技大学）
CAD-RADS scoring of coronary CT angiography with Multi-Axis Vision Transformer: a clinically-inspired deep learning pipeline
University of Pavia（帕维亚大学）；Centro Cardiologico Monzino IRCCS（蒙齐诺心脏病学中心IRCCS）；University of Milan（米兰大学）；IRCCS Istituti Clinici Scientifici Maugeri（IRCCS莫杰里科学临床研究所）
ODySSeI: An Open-Source End-to-End Framework for Automated Detection, Segmentation, and Severity Estimation of Lesions in Invasive Coronary Angiography Images
LTS4 Laboratory, EPFL, Lausanne, Switzerland（EPFL LTS4实验室，瑞士拉瓦尔大学）；Chair of Mathematical Data Science, EPFL, Lausanne, Switzerland（EPFL数学数据科学教授职位，瑞士拉瓦尔大学）；EPFL AI Center, Lausanne, Switzerland（EPFL人工智能中心，瑞士拉瓦尔大学）；Cardiology Department, CHUV, Lausanne, Switzerland（CHUV心内科，瑞士拉瓦尔大学）；Cardiovascular Center, OLV, Aalst, Belgium（OLV心血管中心，比利时艾尔斯特）；School of AIAC, Xi’an Jiaotong-Liverpool University, Suzhou, China（西安交通大学利物浦大学人工智能学院，中国苏州）
Domain-Adapted Fine-Tuning of ECG Foundation Models for Multi-Label Structural Heart Disease Screening
PASSIO Laboratory, North Carolina A&T State University（北卡罗来纳阿塔州立大学PASSIO实验室）；Department of Computer Science, University of Manitoba（曼尼托巴大学计算机科学系）；Harvard T.H. Chan School of Public Health, Harvard University（哈佛大学T.H. Chan公共卫生学院）；Department of Medicine, Jordan University of Science and Technology（约旦科学与技术大学医学院）；Department of Biomedical Engineering, Duke University（达特茅斯大学生物医学工程系）；Harvard Medical School（哈佛医学院）；Beth Israel Deaconess Medical Center, Harvard Medical School（哈佛医学院贝塞斯达以色列德acons医疗中心）
The diagnosis of heart diseases is a difficult task generally addressed by an appropriate examination of patients clinical data. Recently, the use of heart rate variability (HRV) analysis as well as of some machine learning algorithms, has proved to be a valuable support in the diagnosis process. However, till now, ischemic heart disease (IHD) has been diagnosed on the basis of Artificial Neural Networks (ANN) applied only to signs, symptoms and sequential ECG and coronary angiography, an invasive tool, while could be probably identified in a non-invasive way by using parameters extracted from…
