A5084355147- Cardiac Imaging and Diagnostics
- Cardiovascular Function and Risk Factors
- Radiomics and Machine Learning in Medical Imaging
- Advanced MRI Techniques and Applications
- Medical Imaging Techniques and Applications
- Advanced X-ray and CT Imaging
- Cardiac Valve Diseases and Treatments
- Medical Image Segmentation Techniques
- Artificial Intelligence in Healthcare and Education
- Cardiovascular Health and Disease Prevention
- Autopsy Techniques and Outcomes
- 3D Shape Modeling and Analysis
- Medical Imaging and Analysis
- Explainable Artificial Intelligence (XAI)
- Cardiac Arrhythmias and Treatments
- Cardiomyopathy and Myosin Studies
- AI in cancer detection
- Machine Learning in Healthcare
- Cardiac electrophysiology and arrhythmias
- Abdominal Trauma and Injuries
- Cardiac pacing and defibrillation studies
- Cardiovascular Effects of Exercise
- ECG Monitoring and Analysis
- Anomaly Detection Techniques and Applications
- Hemoglobinopathies and Related Disorders
King's College London
2016-2025
Kings Health Partners
2022-2025
University College London
2023
St Thomas' Hospital
2019-2023
King's College School
2018-2023
University Medical Center Utrecht
2023
The King's College
2022
This study sought to develop a fully automated framework for cardiac function analysis from magnetic resonance (CMR), including comprehensive quality control (QC) algorithms detect erroneous output.Analysis of cine CMR imaging using deep learning (DL) could automate ventricular assessment. However, variable image quality, variability in phenotypes disease, and unavoidable weaknesses training DL currently prevent their use clinical practice.The consists pre-analysis QC, followed by algorithm...
Good quality of medical images is a prerequisite for the success subsequent image analysis pipelines. Quality assessment therefore an essential activity and large population studies such as UK Biobank (UKBB), manual identification artefacts those caused by unanticipated motion tedious time-consuming. Therefore, there urgent need automatic techniques. In this paper, we propose method to automatically detect presence motion-related in cardiac magnetic resonance (CMR) cine images. We compare...
Artificial intelligence (AI) techniques have been proposed for automation of cine CMR segmentation functional quantification. However, in other applications AI models shown to potential sex and/or racial bias. The objective this paper is perform the first analysis sex/racial bias AI-based using a large-scale database.
Dysfunction of either the right or left ventricle can lead to heart failure (HF) and subsequent morbidity mortality. We performed a genome-wide association study (GWAS) 16 cardiac magnetic resonance (CMR) imaging measurements biventricular function structure.
Pathogenic and likely pathogenic variants associated with arrhythmogenic right ventricular cardiomyopathy (ARVC), dilated (DCM), hypertrophic (HCM) are recommended to be reported as secondary findings in genome sequencing studies. This provides opportunities for early diagnosis, but also fuels uncertainty variant carriers (G+), since disease penetrance is incomplete. We assessed the prevalence expression of G+ general population.We identified ARVC, DCM and/or HCM 200 643 UK Biobank...
We present a novel multimodal deep learning framework for cardiac resynchronisation therapy (CRT) response prediction from 2D echocardiography and magnetic resonance (CMR) data. The proposed method first uses the 'nnU-Net' segmentation model to extract segmentations of heart over full cycle two modalities. Next, classifier is used CRT prediction, which combines latent spaces models At test time, this can be with data only, whilst taking advantage implicit relationship between CMR features...
Tissue characterisation with CMR parametric mapping has the potential to detect and quantify both focal diffuse alterations in myocardial structure not assessable by late gadolinium enhancement. Native T1 particular shown promise as a useful biomarker support diagnostic, therapeutic prognostic decision-making ischaemic non-ischaemic cardiomyopathies. Convolutional neural networks Bayesian inference are category of artificial which model uncertainty network output. This study presents an...
Cardiac shape modeling is a useful computational tool that has provided quantitative insights into the mechanisms underlying dysfunction in heart disease. The manual input and time required to make cardiac models, however, limits their clinical utility. Here we present an end-to-end pipeline uses deep learning for automated view classification, slice selection, phase anatomical landmark localization, myocardial image segmentation generation of three-dimensional, biventricular models. With...
The aim of this paper is to describe an automated diagnostic pipeline that uses as input only ultrasound (US) data, but at the same time informed by a training database multimodal magnetic resonance (MR) and US image data.We create cardiac motion atlas from three-dimensional (3-D) MR 3-D data followed multi-view machine learning algorithms combine extract most meaningful descriptors for classification dilated cardiomyopathy (DCM) patients using only. More specifically, we propose two based...
Artificial intelligence (AI) techniques have been proposed for automating analysis of short-axis (SAX) cine cardiac magnetic resonance (CMR), but no CMR tool exists to automatically analyse large (unstructured) clinical datasets. We develop and validate a robust AI start-to-end automatic quantification function from SAX in databases.
Abstract Aims Artificial intelligence (AI) methods are being used increasingly for the automated segmentation of cine cardiac magnetic resonance (CMR) imaging. However, these have been shown to be subject race bias; i.e. they exhibit different levels performance races depending on (im)balance data train AI model. In this paper, we investigate source bias, seeking understand its root cause(s). Methods and results We trained models perform classification CMR images and/or segmentations from...
Cardiac motion atlases provide a space of reference in which the motions cohort subjects can be directly compared. Motion used to learn descriptors that are linked different pathologies and subsequently for diagnosis. To date, all such have been formed applied using data from same modality. In this work we propose framework build multimodal cardiac atlas 3D magnetic resonance (MR) ultrasound (US) data. Such an will benefit complementary features derived two modalities, furthermore, it could...
Abstract Aims Existing strategies that identify post-infarct ventricular tachycardia (VT) ablation target either employ invasive electrophysiological (EP) mapping or non-invasive modalities utilizing the electrocardiogram (ECG). Their success relies on localizing sites critical to maintenance of clinical arrhythmia, not always recorded 12-lead ECG. Targeting VT by electrograms (EGM) recordings stored in implanted devices may aid planning, enhancing safety and speed potentially reducing need...
Cardiovascular magnetic resonance myocardial feature tracking (CMR-FT) is a promising method for quantification of cardiac function from standard steady-state free precession (SSFP) images. However, currently available techniques require operator dependent and time-consuming manual intervention, limiting reproducibility clinical use. In this paper, we propose fully automated pipeline to compute left ventricular (LV) longitudinal radial strain 2- 4-chamber cine acquisitions, LV...
Background Automated analysis of cardiovascular magnetic resonance images provides the potential to assess aortic distensibility in large populations. The aim this study was compare prediction events by automated with those other simple measures stiffness suitable for population screening. Methods and Results Aortic measured from segmentation cine using artificial intelligence 8435 participants. associations distensibility, brachial pulse pressure, index (obtained finger...