Recent advancements in Artificial Intelligence (AI) have significantly influenced the field of Cardiovascular Disease (CVD) analysis, particularly in image-based diagnostics. Our paper presents an extensive review of AI applications in image-based CVD analysis, offering insights into its current state and future potential. We systematically categorize the literature based on the primary anatomical structures related to CVD, dividing them into non-vessel structures (such as ventricles and atria) and vessel structures (including the aorta and coronary arteries). This categorization provides a structured approach to explore various imaging modalities like Computed tomography (CT) and Magnetic Resonance Imaging (MRI), which are commonly used in CVD research. Our review encompasses these modalities, giving a broad perspective on the diverse imaging techniques integrated with AI for CVD analysis. We conclude with an examination of the challenges and limitations inherent in current AI-based CVD analysis methods and suggest directions for future research to overcome these hurdles.
Rensselaer Polytechnic Institute（伦斯勒理工学院）；Massachusetts General Hospital（麻省总医院）；Harvard Medical School（哈佛医学院）；Niskayuna High School（尼斯卡尤纳高中）
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population. Low dose computed tomography (LDCT) for lung cancer screening offers an opportunity for simultaneous CVD risk estimation in at-risk patients. Our deep learning CVD risk prediction model, trained with 30,286 LDCTs from the National Lung Cancer Screening Trial, achieved an area under the curve (AUC) of 0.871 on a separate test set of 2,085 subjects and identified patients with high CVD mortality risks (AUC of 0.768). We validated our model against ECG-gated cardiac CT based markers, includin…
Purpose: To demonstrate that retinal microvasculature per se is a reliable biomarker for Diabetic Retinopathy (DR) and, by extension, cardiovascular diseases. Methods: Deep Learning Convolutional Neural Networks (CNN) applied to color fundus images for semantic segmentation of the blood vessels and severity classification on both vascular and full images. Vessel reconstruction through harmonic descriptors is also used as a smoothing and de-noising tool. The mathematical background of the theory is also outlined. Results: For diabetic patients, at least 93.8% of DR No-Refer vs. Refer classifica…
Artificial Intelligence in Assessing Cardiovascular Diseases and Risk Factors via Retinal Fundus Images: A Review of the Last Decade
Nikookari Eye Hospital（尼库卡里眼科医院）；Tabriz University of Medical Sciences（大不里士医科大学）；Cardiovascular Research Center（心血管研究中心）；Research Center for Evidence‑Based Medicine（循证医学研究中心）；Iranian EBM Centre: A Joanna Briggs Institute Affiliated Group（伊朗循证医学中心：乔安娜·布里格斯研究所附属机构）；Tabriz Islamic Art University（大不里士伊斯兰艺术大学）；Institute for Intelligent Systems Research and Innovation (IISRI)（智能系统研究与创新研究所（IISRI））；Deakin University（迪肯大学）
Integrating electrocardiogram and fundus images for early detection of cardiovascular diseases
University of Petroleum and Energy Studies（石油与能源研究大学）；SRM University AP（SRM大学阿普拉分校）；Sri Kalpana Heart Care（斯里卡尔帕纳心脏护理中心）；Centre for High Impact Neuroscience and Translational Applications, TCG Crest（TCG Crest 高影响力神经科学与转化应用中心）；Ashwini Eye Care（阿什维尼眼科护理中心）
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…
Integrating Deep Learning with Fundus and Optical Coherence Tomography for Cardiovascular Disease Prediction
University of Leeds（利兹大学）；University of Manchester（曼彻斯特大学）；NIHR Manchester Biomedical Research Centre（NIHR曼彻斯特生物医学研究中心）；Manchester Academic Health Science Centre（曼彻斯特学术健康科学中心）；Christabel Pankhurst Institute（克里斯塔贝尔·潘克赫斯特研究所）
BAAI Cardiac Agent: An intelligent multimodal agent for automated reasoning and diagnosis of cardiovascular diseases from cardiac magnetic resonance imaging
Beijing Academy of Artificial Intelligence（北京智源人工智能研究院）；Department of Radiology, Beijing Anzhen Hospital, Beijing Institute of Heart, Lung & Vascular Diseases, Capital Medical University（首都医科大学附属北京安贞医院放射科，北京市心肺血管疾病研究所）；Department of MR, the First Affiliated Hospital, Henan Medical University（河南医科大学第一附属医院磁共振科）；Department of Cardiology, Clinical Center for Coronary Heart Disease, Beijing Institute of Heart, Lung and Blood Vessel Disease, Beijing Anzhen Hospital, Capital Medical University（首都医科大学附属北京安贞医院心内科，冠心病临床中心，北京市心肺血管疾病研究所）；Department of Cardiac Surgery, Beijing Anzhen Hospital, Institute of Heart, Lung and Vascular Diseases, Capital Medical University（首都医科大学附属北京安贞医院心脏外科，心肺血管疾病研究所）
Electrocardiography (ECG), an electrical measurement which captures cardiac activities, is the gold standard for diagnosing cardiovascular disease (CVD). However, ECG is infeasible for continuous cardiac monitoring due to its requirement for user participation. By contrast, photoplethysmography (PPG) provides easy-to-collect data, but its limited accuracy constrains its clinical usage. To combine the advantages of both signals, recent studies incorporate various deep learning techniques for the reconstruction of PPG signals to ECG; however, the lack of contextual information as well as the lim…
Purpose: To create and evaluate the accuracy of an artificial intelligence Deep learning platform (ORAiCLE) capable of using only retinal fundus images to predict both an individuals overall 5 year cardiovascular risk (CVD) and the relative contribution of the component risk factors that comprise this risk. Methods: We used 165,907 retinal images from a database of 47,236 patient visits. Initially, each image was paired with biometric data age, ethnicity, sex, presence and duration of diabetes a HDL/LDL ratios as well as any CVD event wtihin 5 years of the retinal image acquisition. A risk sco…
A Joint Representation Using Continuous and Discrete Features for Cardiovascular Diseases Risk Prediction on Chest CT Scans
DAMO Academy, Alibaba Group（阿里巴巴集团达摩院）；Tsinghua University（清华大学）；Hupan Laboratory（湖畔实验室）；Beijing Key Laboratory of Precision Medicine for Chronic Heart Failure（慢性心力衰竭精准医学北京市重点实验室）；National Engineering Research Center for Medical Big Data Application Technology（国家医疗大数据应用技术工程研究中心）
Predicting risk of cardiovascular disease using retinal OCT imaging
University of Leeds（利兹大学）；Research Institute for Signals, Systems and Computational Intelligence（信号、系统与计算智能研究所）；CONICET-UNL（阿根廷科学技术研究委员会-国立洛马大学）；University of Manchester（曼彻斯特大学）；Manchester University NHS Foundation Trust（曼彻斯特大学NHS基金会信托基金）；European Molecular Biology Laboratory（欧洲分子生物学实验室）；European Bioinformatics Institute (EMBL-EBI)（欧洲生物信息研究所）；NIHR Manchester Biomedical Research Centre（NIHR曼彻斯特生物医学研究中心）；Manchester Academic Health Science Centre（曼彻斯特学术健康科学中心）
提出基于 VAE 与随机森林的视网膜 OCT 自监督方法，结合临床数据预测心肌梗死或卒中风险，性能优于 QRISK3，并揭示脉络膜层为关键特征。
Dual-Phase Cross-Modal Contrastive Learning for CMR-Guided ECG Representations for Cardiovascular Disease Assessment
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）；Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center（放射医学与核医学系，阿姆斯特丹大学医学中心）；Department of Radiology, Mayo Clinic, Rochester（放射医学系，梅奥诊所，罗切斯特）
Imperial College London（帝国理工学院）；UK Dementia Research Institute（英国痴呆症研究所）；Technical University of Munich（慕尼黑工业大学）；Fudan University（复旦大学）
Cardiovascular disease, the leading cause of death globally, is an age-related disease. Understanding the morphological and functional changes of the heart during ageing is a key scientific question, the answer to which will help us define important risk factors of cardiovascular disease and monitor disease progression. In this work, we propose a novel conditional generative model to describe the changes of 3D anatomy of the heart during ageing. The proposed model is flexible and allows integration of multiple clinical factors (e.g. age, gender) into the generating process. We train the model …
AI-based Aortic Vessel Tree Segmentation for Cardiovascular Diseases Treatment: Status Quo
Graz University of Technology（格拉茨工业大学）；Computer Algorithms for Medicine Laboratory（医学计算机算法实验室）；University Medicine Essen (AöR)（埃森大学医院（AöR））；Affiliated Dongyang Hospital of Wenzhou Medical University（温州医科大学附属东阳医院）
The Role of AI in Early Detection of Life-Threatening Diseases: A Retinal Imaging Perspective
Naif Arab University for Security Sciences（奈伊夫阿拉伯安全科学大学）；Charles Sturt University（查尔斯特大学）；Mohamed bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）
Faculty of Electrical Engineering, Computer Science and Information Technology Osijek（奥西耶克电气工程、计算机科学与信息技术学院）
Epicardial adipose tissue is a type of adipose tissue located between the heart wall and a protective layer around the heart called the pericardium. The volume and thickness of epicardial adipose tissue are linked to various cardiovascular diseases. It is shown to be an independent cardiovascular disease risk factor. Fully automatic and reliable measurements of epicardial adipose tissue from CT scans could provide better disease risk assessment and enable the processing of large CT image data sets for a systemic epicardial adipose tissue study. This paper proposes a method for fully automatic …
Artificial Intelligence and Diabetes Mellitus: An Inside Look Through the Retina
Tabriz University of Medical Sciences（大不里士医科大学）；Nikookari Eye Center（尼库卡里眼科中心）；Tabriz Islamic Art University（大不里士伊斯兰艺术大学）；Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University（迪肯大学智能系统研究与创新研究所）；National Heart Centre Singapore（新加坡国家心脏中心）；Duke-NUS Medical School（杜克-新加坡国立大学医学院）；The George Institute for Global Health（乔治全球健康研究所）；Sydney Medical School, University of Sydney（悉尼大学悉尼医学院）；School of Mathematics, Physics, and Computing, University of Southern Queensland（南昆士兰大学数学、物理与计算学院）；Centre for Health Research, University of Southern Queensland（南昆士兰大学健康研究中心）
Among the most impactful diabetic complications are diabetic retinopathy, the leading cause of blindness among working class adults, and cardiovascular disease, the leading cause of death worldwide. This study describes the development of improved machine learning based screening of these conditions. First, a random forest model was developed by retrospectively analyzing the influence of various risk factors (obtained quickly and non-invasively) on cardiovascular risk. Next, a deep-learning model was developed for prediction of diabetic retinopathy from retinal fundus images by a modified and …
University of Birjand（比尔詹德大学）；Deakin University（迪肯大学）；Iran University of Medical Sciences（伊朗医科大学）；Omid hospital（奥米德医院）；Maragheh Faculty of Medical Sciences（马拉盖医学院）；Universiti Kebangsaan Malaysia（马来西亚国民大学）
Cardiovascular disease is one of the most challenging diseases in middle-aged and older people, which causes high mortality. Coronary artery disease (CAD) is known as a common cardiovascular disease. A standard clinical tool for diagnosing CAD is angiography. The main challenges are dangerous side effects and high angiography costs. Today, the development of artificial intelligence-based methods is a valuable achievement for diagnosing disease. Hence, in this paper, artificial intelligence methods such as neural network (NN), deep neural network (DNN), and Fuzzy C-Means clustering combined wit…
