Efficient and Robust Assessment of Cardiovascular Disease Using Machine Learning and Ultrasound Imaging

利用机器学习和超声成像对心血管疾病进行高效、稳健的评估

• 批准号: EP/R005516/1
• 负责人: Andrew King
• 金额: $ 39.5万
• 依托单位: King's College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FR005516%2F1
• 关键词: Efficient; Robust; Assessment; Cardiovascular; Disease

Heart disease is the number one killer in the world. Currently the best way of diagnosing heart disease and planning its treatment is to use a magnetic resonance imaging (MRI) scanner. However, MRI scanners are expensive and not typically used for scanning hearts in most UK hospitals. Therefore, the best diagnosis and treatment are not available to all patients. Currently the most common way of assessing heart disease is through the use of an ultrasound scanner. Although ultrasound has many advantages, it does not have such good image quality as MRI and so there are difficulties associated with its use in heart disease management. If the 'gold standard' quality of assessment from MRI could somehow be made feasible using ultrasound it would have great potential benefits for patients.This is the aim of this project. We aim to use state-of-the-art machine learning techniques combined with rich multimodal imaging data to produce a computer model of heart disease and its associations with heart shape and motion. By incorporating MRI as well as ultrasound imaging data into the model we can exploit the power of MRI based only on ultrasound imaging. This would make possible a low cost and easy clinical pathway to the best care possible.

心脏病是世界上的头号杀手。目前，诊断心脏病和计划治疗的最佳方法是使用磁共振成像（MRI）扫描仪。然而，MRI扫描仪价格昂贵，并且在大多数英国医院中通常不用于扫描心脏。因此，并非所有患者都能获得最佳诊断和治疗。目前，评估心脏病的最常见方法是使用超声扫描仪。虽然超声波有许多优点，但它没有MRI那样好的图像质量，因此在心脏病管理中使用它存在困难。如果磁共振成像的“黄金标准”评估质量可以通过超声技术实现，那么它将为患者带来巨大的潜在利益，这也是本项目的目标。我们的目标是使用最先进的机器学习技术，结合丰富的多模态成像数据，生成心脏病及其与心脏形状和运动的关联的计算机模型。通过将MRI以及超声成像数据合并到模型中，我们可以利用仅基于超声成像的MRI的能力。这将使低成本和简单的临床途径成为可能，从而获得最佳护理。

项目成果

An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases.

• DOI: 10.1093/ehjdh/ztad044
• 发表时间: 2023-10
• 期刊: European heart journal. Digital health

Fairness in AI: are deep learning-based CMR segmentation algorithms biased?

AI 公平性：基于深度学习的 CMR 分割算法是否存在偏见？

• DOI: 10.1093/eurheartj/ehab724.3055
• 发表时间: 2021
• 期刊: European Heart Journal
• 影响因子: 39.3
• 作者: Puyol Anton E

The Role of AI in Characterizing the DCM Phenotype.

• DOI: 10.3389/fcvm.2021.787614
• 发表时间: 2021
• 期刊: Frontiers in cardiovascular medicine
• 影响因子: 3.6
• 作者: Asher C;Puyol-Antón E;Rizvi M;Ruijsink B;Chiribiri A;Razavi R;Carr-White G
• 通讯作者: Carr-White G

Large-scale, multi-vendor, multi-protocol, quality-controlled analysis of clinical cine CMR using artificial intelligence

使用人工智能对临床电影 CMR 进行大规模、多供应商、多协议、质量控制分析

• DOI: 10.1093/ehjci/jeab090.046
• 发表时间: 2021
• 期刊: European Heart Journal - Cardiovascular Imaging
• 作者: Mariscal Harana J