Assessment of Clonal Hematopoiesis of Indeterminate Potential and Future Cardiomyopathy from Cardiac Magnetic Resonance Imaging using Deep Learning in a Cardio-oncology Population
Jiarui Xing, Sangeon Ryu, Shawn Ahn, Jeacy Espinoza, James L. Cross, Stephanie Halene, James S. Duncan, Alokkumar Jha, Jennifer M Kwan, Nicha C. Dvornek
We propose a novel deep learning framework to identify clonal hematopoiesis of indeterminate potential (CHIP), a somatic mutation condition associated with adverse cardiovascular outcomes, using routine cardiac magnetic resonance (CMR) imaging. Utilizing 152 multi-view late gadolinium enhancement (LGE) scans from 136 cardio-oncology patients, we developed a convolutional neural network to (1) detect CHIP status and (2) stratify the risk of future cardiomyopathy specifically within the CHIP-positive cohort. To ensure robustness, we performed rigorous feature importance analysis to rule out reliance on demographic confounders such as age and immune checkpoint inhibitor usage. The model achieved an AUC of 0.71 for CHIP detection and, notably, an AUC of 0.87 for predicting future cardiomyopathy in CHIP-positive patients, significantly outperforming demographic-only baselines. These results demonstrate the feasibility of using LGE-CMR signatures as a non-invasive "radiogenomic" screening tool, potentially enabling accessible risk stratification and precision medicine for high-risk cardiovascular populations.
Hospital Virgen de la Arrixaca（拉阿里克萨卡圣母医院）；University of Murcia（穆尔西亚大学）
Left ventricular non-compaction (LVNC) is a rare cardiomyopathy characterized by abnormal trabeculations in the left ventricle cavity. Although traditional computer vision approaches exist for LVNC diagnosis, deep learning-based tools could not be found in the literature. In this paper, a first approach using convolutional neural networks (CNNs) is presented. Four CNNs are trained to automatically segment the compacted and trabecular areas of the left ventricle for a population of patients diagnosed with Hypertrophic cardiomyopathy. Inference results confirm that deep learning-based approaches…
Zebrafish is a powerful and widely-used model system for a host of biological investigations including cardiovascular studies and genetic screening. Zebrafish are readily assessable during developmental stages; however, the current methods for quantification and monitoring of cardiac functions mostly involve tedious manual work and inconsistent estimations. In this paper, we developed and validated a Zebrafish Automatic Cardiovascular Assessment Framework (ZACAF) based on a U-net deep learning model for automated assessment of cardiovascular indices, such as ejection fraction (EF) and fraction…
Deep learning models can enable accurate and efficient disease diagnosis, but have thus far been hampered by the data scarcity present in the medical world. Automated diagnosis studies have been constrained by underpowered single-center datasets, and although some results have shown promise, their generalizability to other institutions remains questionable as the data heterogeneity between institutions is not taken into account. By allowing models to be trained in a distributed manner that preserves patients' privacy, federated learning promises to alleviate these issues, by enabling diligent …
UNSW Sydney（新南威尔士大学悉尼分校）；University of Granada（格拉纳达大学）；K. N. Toosi University of Technology（K. N. 图西理工大学）；Deakin University（迪肯大学）；University of Technology Sydney（悉尼科技大学）；University of Leicester（莱斯特大学）；University of Cambridge（剑桥大学）；The University of New South Wales（新南威尔士大学）；Macquarie University（麦考瑞大学）；Ngee Ann Polytechnic（义安理工学院）；Asia University（亚洲大学）；Singapore University of Social Sciences（新加坡社科大学）
In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods us…
Translating Electrocardiograms to Cardiac Magnetic Resonance Imaging Useful for Cardiac Assessment and Disease Screening: A Multi-Center Study
College of Computer Science and Technology, Zhejiang University（浙江大学计算机科学与技术学院）；Department of Vascular Surgery, The First Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第一医院血管外科）；Department of Cardiology, The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院心内科）；Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine（浙江大学医学院附属第一医院放射科）；Department of Vascular Surgery, Quzhou People’s Hospital（衢州人民医院血管外科）；Department of Cardiology, The Second Affiliated Hospital of Zhejiang University School of Medicine（浙江大学医学院附属第二医院心内科）；China Ship Scientific Research Center（中国船舶科学研究院）；Guangdong Transtek Medical Electronics Co., Ltd.（广东 Transtek 医疗电子有限公司）
A Generalizable Deep Learning System for Cardiac MRI
Division of Cardiovascular Surgery, Department of Surgery, University of Pennsylvania（宾夕法尼亚大学心血管外科 division）；Department of Cardiothoracic Surgery, Stanford University（斯坦福大学心胸外科 department）；Department of Radiology, Medstar Georgetown University Hospital（梅奥医疗中心乔治城大学医院放射科 department）；Department of Radiology and Biomedical Imaging, University of California, San Francisco（加州大学旧金山分校放射科和生物医学成像 department）；Bunkerhill Health, San Francisco（旧金山布纳克希尔健康机构）；Department of Radiology, University of Pennsylvania（宾夕法尼亚大学放射科 department）；Division of Cardiovascular Medicine, Department of Medicine, University of Pennsylvania（宾夕法尼亚大学心血管医学 division）；Division of Cardiovascular Medicine, Department of Medicine, Genetics, and Biomedical Data Science, Stanford University（斯坦福大学心血管医学 division）；Department of Radiology, Medicine, and Biomedical Data Science, Stanford University（斯坦福大学放射科、医学和生物医学数据科学 department）
Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；University of Leeds（利兹大学）
Left ventricle segmentation and morphological assessment are essential for improving diagnosis and our understanding of cardiomyopathy, which in turn is imperative for reducing risk of myocardial infarctions in patients. Convolutional neural network (CNN) based methods for cardiac magnetic resonance (CMR) image segmentation rely on supervision with pixel-level annotations, and may not generalize well to images from a different domain. These methods are typically sensitive to variations in imaging protocols and data acquisition. Since annotating multi-sequence CMR images is tedious and subject …
University of Bourgogne Franche-Comté（勃艮第-弗朗什孔泰大学）；University Hospital of Dijon（第戎大学医院）；CASIS company（CASIS公司）
Cardiac left ventricular (LV) segmentation from short-axis MRI acquired 10 minutes after the injection of a contrast agent (LGE-MRI) is a necessary step in the processing allowing the identification and diagnosis of cardiac diseases such as myocardial infarction. However, this segmentation is challenging due to high variability across subjects and the potential lack of contrast between structures. Then, the main objective of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. To this end, 2.5 D residual neural n…
Leveraging Cardiac Imaging to Improve ECG-Based Detection of Chagas Disease in Resource-Constrained Settings
Amsterdam University Medical Center（阿姆斯特丹大学医学中心）；University of Amsterdam（阿姆斯特丹大学）；ETH Zurich（苏黎世联邦理工学院）；Amsterdam Cardiovascular Sciences（阿姆斯特丹心血管科学研究院）
King's College London（伦敦国王学院）；Imperial College London（帝国理工学院）；Guy’s and St Thomas' Hospital（盖伊和圣托马斯医院）
Advances in deep learning (DL) have resulted in impressive accuracy in some medical image classification tasks, but often deep models lack interpretability. The ability of these models to explain their decisions is important for fostering clinical trust and facilitating clinical translation. Furthermore, for many problems in medicine there is a wealth of existing clinical knowledge to draw upon, which may be useful in generating explanations, but it is not obvious how this knowledge can be encoded into DL models - most models are learnt either from scratch or using transfer learning from a dif…
Fudan University（复旦大学）；Shanghai Jiao Tong University（上海交通大学）；Imperial College London（帝国理工学院）；Universitat de Barcelona（巴塞罗那大学）；Friedrich-Alexander-Universität Erlangen-Nürnberg（埃尔朗根-纽伦堡弗里德里希-亚历山大大学）；Harbin Institute of Technology（哈尔滨工业大学）；Southern University of Science and Technology（南方科技大学）；Technical University of Munich（慕尼黑工业大学）；INRIA（法国国家信息与自动化研究所）；Université Côte d’Azur（蔚蓝海岸大学）；NVIDIA（英伟达）；Xiamen University（厦门大学）；Sichuan University（四川大学）；Tencent AI Lab（腾讯人工智能实验室）
Accurate computing, analysis and modeling of the ventricles and myocardium from medical images are important, especially in the diagnosis and treatment management for patients suffering from myocardial infarction (MI). Late gadolinium enhancement (LGE) cardiac magnetic resonance (CMR) provides an important protocol to visualize MI. However, automated segmentation of LGE CMR is still challenging, due to the indistinguishable boundaries, heterogeneous intensity distribution and complex enhancement patterns of pathological myocardium from LGE CMR. Furthermore, compared with the other sequences LG…
ScarNet: A Novel Foundation Model for Automated Myocardial Scar Quantification from LGE in Cardiac MRI
Northwestern University（西北大学）；Feinberg School of Medicine（费林伯格医学院）；University of Miami Miller School of Medicine（迈阿密大学米勒医学院）；Cedars-Sinai Medical Center（西达赛奈医疗中心）
