Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health. The aim of this study is to analyze the impact of systolic blood pressure (SBP) on cardiac function while preserving the interpretability of the model using known clinical biomarkers in a large cohort of the UK Biobank population. We propose a novel framework that combines deep learning based estimation of interpretable clinical biomarkers from cardiac cine MR data with a variational autoencoder (VAE). The VAE architecture integrates a regression loss in the latent space, which enables the progression of cardiac health with SBP to be learnt. Results on 3,600 subjects from the UK Biobank show that the proposed model allows us to gain important insight into the deterioration of cardiac function with increasing SBP, identify key interpretable factors involved in this process, and lastly exploit the model to understand patterns of positive and adverse adaptation of cardiac function.
Ingenuity Labs Research Institute（Ingenuity Labs 研究院）
Electrocardiogram (ECG) is the electrical measurement of cardiac activity, whereas Photoplethysmogram (PPG) is the optical measurement of volumetric changes in blood circulation. While both signals are used for heart rate monitoring, from a medical perspective, ECG is more useful as it carries additional cardiac information. Despite many attempts toward incorporating ECG sensing in smartwatches or similar wearable devices for continuous and reliable cardiac monitoring, PPG sensors are the main feasible sensing solution available. In order to tackle this problem, we propose CardioGAN, an advers…
Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their applicability in clinical practice. In this work we introduce a convolutional neural network model for identifying disease in temporal sequences of cardiac MR segmentations which is interpretable in terms of clinically familiar measurements. The model is based around a variational autoencoder, reducing the input into a low-dimensional latent space in which classification occurs. We then use the recently developed `conce…
Towards early diagnosis of Alzheimer's disease: Advances in immune-related blood biomarkers and computational modeling approaches
Fraunhofer Institute for Algorithms and Scientific Computing (SCAI)（弗劳恩霍夫算法与科学计算研究所（SCAI））；University of Bonn（波恩大学）；Tel Aviv University（特拉维夫大学）；Hospital Clínic de Barcelona（巴塞罗那临床医院）；University of Barcelona（巴塞罗那大学）；ImmunoBrain Checkpoint Ltd.（ImmunoBrain Checkpoint有限公司）；Sagol School of Neuroscience（萨戈尔神经科学学院）
With the advent of Cardiac Cine Magnetic Resonance (CMR) Imaging, there has been a paradigm shift in medical technology, thanks to its capability of imaging different structures within the heart without ionizing radiation. However, it is very challenging to conduct pre-operative planning of minimally invasive cardiac procedures without accurate segmentation and identification of the left ventricle (LV), right ventricle (RV) blood-pool, and LV-myocardium. Manual segmentation of those structures, nevertheless, is time-consuming and often prone to error and biased outcomes. Hence, automatic and c…
Institute of Clinical Molecular Biology, Christian-Albrecht-University of Kiel（基尔大学临床分子生物学研究所）；Center for Bioinformatics, Saarland University（萨尔大学生物信息学中心）
A blood cell lineage consists of several consecutive developmental stages from the pluri- or multipotent stem cell to a state of terminal differentiation. Despite their importance for human biology, the regulatory pathways and gene networks that govern these differentiation processes are not yet fully understood. This is in part due to challenges associated with delineating the interactions between transcription factors (TFs) and their target genes. A possible path forward in this issue is provided by increasingly available expression data as a basis for linking differentiation stages and gene…
NVIDIA（英伟达）；Lenovo Research（联想研究院）；Piedmont Heart Institute（皮埃蒙特心脏研究所）；Tencent Medical AI Lab（腾讯医疗人工智能实验室）；University of California, Irvine（加州大学欧文分校）
Echocardiography has become routinely used in the diagnosis of cardiomyopathy and abnormal cardiac blood flow. However, manually measuring myocardial motion and cardiac blood flow from echocardiogram is time-consuming and error-prone. Computer algorithms that can automatically track and quantify myocardial motion and cardiac blood flow are highly sought after, but have not been very successful due to noise and high variability of echocardiography. In this work, we propose a neural multi-scale self-supervised registration (NMSR) method for automated myocardial and cardiac blood flow dense track…
Real-time deep learning phase imaging flow cytometer reveals blood cell aggregate biomarkers for haematology diagnostics
Technical University of Munich（慕尼黑工业大学）；TUMCREATE；Bioinformatics Institute, Agency for Science, Technology and Research（科技研究局生物信息研究所）；TUM University Hospital（TUM大学医院）；National University of Singapore（新加坡国立大学）；National University Hospital（新加坡国立大学医院）；German Center for Infection Research (DZIF)（德国感染研究中心）；Infectious Diseases Labs (AIDL), Agency for Science, Technology and Research（科技研究局传染病实验室）；Nanyang Technological University（南洋理工大学）；Centre for Frontier AI Research, Agency for Science, Technology and Research（科技研究局前沿人工智能研究中心）
Learning Normal Representations for Blood Biomarkers
Department of Biomedical Informatics, Harvard Medical School（哈佛医学院生物医学信息学系）；Department of Systems Biology, Harvard Medical School（哈佛医学院系统生物学系）；Department of Medicine, Brigham and Women’s Hospital（布里洛妇产科医院医学系）；Department of Mathematics, Johns Hopkins University（约翰霍普金斯大学数学系）；Computational Health Informatics Program (CHIP), Boston Children’s Hospital（波士顿儿童医院计算健康信息学计划）；The Ivan and Francesca Berkowitz Family Living Laboratory Collaboration at Harvard Medical School and Clalit Research Institute（哈佛医学院伊万和弗rancesca伯克伍德家庭生活实验室合作项目及克劳斯研究机构）；Clalit Research Institute, Innovation Division, Clalit Health Services（克劳斯研究机构创新部门，克劳斯健康服务）；Faculty of Computer and Information Science, Ben Gurion University（本· Gurion大学计算机与信息科学系）
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 Across the Cardiac Amyloidosis Diagnostic and Management Pathway: From Single-Modality Detection to Multimodal Clinical Integration
Michigan Technological University（密歇根理工大学）；International Atomic Energy Agency（国际原子能机构）；University of Chicago（芝加哥大学）；Cook County Health and Hospitals System（库克县健康与医院系统）；Rush Medical College（拉什医学院）
University of Edinburgh（爱丁堡大学）；Imperial College London（帝国理工学院）；University of Essex（埃塞克斯大学）
Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as "modalities"). As each modality is designed to offer different anatomical and functional clinical information, there are evident disparities in the imaging content across modalities. Inter- and intra-modality affine and non-rigid image registration is an essential medical image analysis process in clinical imaging, as for example before imaging biomarkers need to be derived and clinically evaluated across different MRI modalities, time phases and slices. Although commonly needed in real clinical scenarios, …
An AI tool for automated analysis of large-scale unstructured clinical cine CMR databases
King’s College London（伦敦国王学院）；Guy’s and St Thomas’ NHS Foundation Trust（盖伊和圣托马斯NHS基金会信托）；Duke University（杜克大学）；Barts Health NHS Trust（巴特健康NHS基金会信托）；Alan Turing Institute（阿兰·图灵研究所）；University Medical Center Utrecht（乌得勒支大学医学中心）
Predicting Lethal Outcome (Cause) And Understanding Key Biomarkers Linked With Acute Myocardial Infarction Using Deep Artificial Neural Network And Ensemble Of Machine Learning Methodologies
