Machine Learning and Medical Image Analysis for Point-of-Care Ultrasound Systems

用于床旁超声系统的机器学习和医学图像分析

• 批准号: 2288295
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2288295
• 关键词: Machine; Learning; Medical; Image; Analysis

Access to diagnostic ultrasound (US) in low and middle income countries (LMICs) is impeded because of a lack of experienced sonographers and the expertise required to scan well. Furthermore, conventional US systems carry the burden of high equipment costs reducing their feasibility for use in such environments. Statistics show that worldwide, 99% of maternal deaths occur in LMICs indicating an important unmet clinical need to provide US-based diagnosis [1]. Recent state-of-the-art advances have engineered low-cost US devices which show high potential for use in point-of-care (POC) scenarios. Similarly, advancements in machine learning architectures now offer superior performance over predicate solutions and open realms of possibility in computer pattern recognition of 2D US images and video. There is a real potential for automated computer analysis of US to be deployed within POC US systems in countries such as India as well as in Africa, thus addressing the skills crisis and diagnostic need.The proposed doctoral research aims to develop and evaluate a novel automated image analysis framework that utilises a simplified US scanning protocol of multiple scanning sweeps to provide a clinical decision support tool for healthcare workers unfamiliar with ultrasound. 2D US images and video are rich in spatial-temporal features and acoustic patterns and the research will consider how these can be extracted and combined to good effect within machine learning architectures to reveal important pieces of clinical information. A first key challenge of this project will be to translate the clinical criteria, obtained from the literature and clinical collaborators, into a machine learning framework. A second challenge will be how to design and implement appropriate machine learning architecture for the chosen clinical tasks. A deep learning approach is most likely to be a feasible method. Finally, feasibility studies will be performed in collaboration with clinical partners in the UK and India to evaluate the developed methods and consider the potential usability in practice.The research falls within the EPSRC's 'Healthcare Technologies' and 'Engineering' research themes. In particular, the research develops work within 'Image and Vision Computing', 'Human-Computer Interaction', and 'Medical Imaging' sub-themes. The research also fits into the EPSRC Grand Challenges through 'Transforming Community Health and Care' and 'Optimising Treatment'.The doctoral research will be conducted associated with the GCRF-funded CALOPUS Project (Computer-Assisted Low-cost Point-of-Care Ultrasound: EP/R013853/1) which is a joint collaboration between the Institute of Biomedical Engineering (IBME) and Nuffield Department of Women's and Reproductive Health, University of Oxford, and the Translational Health Science and Technology Institute (THSTI), Faridabad, India.

由于缺乏经验丰富的超声技师和良好扫描所需的专业知识，低收入和中等收入国家难以获得超声诊断。此外，传统的美国系统承担着高设备成本的负担，降低了它们在这种环境下使用的可行性。统计数据显示，全世界99%的孕产妇死亡发生在中低收入国家，这表明提供基于美国的诊断的重要临床需求尚未得到满足。最近最先进的技术已经设计出低成本的美国设备，在护理点（POC）场景中显示出很高的使用潜力。同样，机器学习架构的进步现在在美国2D图像和视频的计算机模式识别方面提供了优于谓词解决方案的性能和开放的可能性领域。在印度和非洲等国家的POC US系统中部署US的自动化计算机分析确实有潜力，从而解决技能危机和诊断需求。拟议的博士研究旨在开发和评估一种新的自动图像分析框架，该框架利用简化的美国多次扫描扫描协议，为不熟悉超声的医护人员提供临床决策支持工具。2D美国图像和视频具有丰富的时空特征和声学模式，研究将考虑如何在机器学习架构中提取和组合这些特征，以获得良好的效果，从而揭示重要的临床信息。该项目的第一个关键挑战将是将从文献和临床合作者获得的临床标准转化为机器学习框架。第二个挑战是如何为选定的临床任务设计和实施适当的机器学习架构。深度学习方法最有可能是一种可行的方法。最后，可行性研究将与英国和印度的临床合作伙伴合作进行，以评估开发的方法并考虑在实践中的潜在可用性。这项研究属于EPSRC的“医疗技术”和“工程”研究主题。特别是，该研究开发了“图像和视觉计算”、“人机交互”和“医学成像”子主题的工作。这项研究也符合EPSRC通过“改变社区卫生和护理”和“优化治疗”的大挑战。博士研究将与gcrf资助的CALOPUS项目（计算机辅助低成本护理点超声：EP/R013853/1）一起进行，该项目是牛津大学生物医学工程研究所（IBME）和纳菲尔德妇女与生殖健康系以及印度法里达巴德的转化健康科学与技术研究所（THSTI）之间的联合合作。

项目成果

Developing Clinical Artificial Intelligence for Obstetric Ultrasound to Improve Access in Underserved Regions: Protocol for a Computer-Assisted Low-Cost Point-of-Care UltraSound (CALOPUS) Study.

• DOI: 10.2196/37374
• 发表时间: 2022-09-01
• 期刊: JMIR RESEARCH PROTOCOLS
• 影响因子: 1.7
• 作者: Self, Alice;Chen, Qingchao;Desiraju, Bapu Koundinya;Dhariwal, Sumeet;Gleed, Alexander;Mishra, Divyanshu;Thiruvengadam, Ramachandran;Chandramohan, Varun;Craik, Rachel;Wilden, Elizabeth;Khurana, Ashok;CALOPUS Study Grp, Shinjini;Bhatnagar, Shinjini;Papageorghiou, Aris T.;Noble, J. Alison
• 通讯作者: Noble, J. Alison

Automatic Image Guidance for Assessment of Placenta Location in Ultrasound Video Sweeps.

• DOI: 10.1016/j.ultrasmedbio.2022.08.006
• 发表时间: 2022-10
• 期刊: Ultrasound in medicine & biology
• 影响因子: 2.9
• 作者: A. Gleed;Qingchao Chen;James Jackman;D. Mishra;V. Chandramohan;A. Self;S. Bhatnagar;A. Papageorghiou;J. Noble

Medical image analysis for simplified ultrasound protocols

用于简化超声协议的医学图像分析

• DOI: 10.5287/ora-4jvegrkdd
• 发表时间: 2023
• 作者: Gleed A

VP18.01: Machine learning applied to the standardised six-step approach for placental localisation in basic obstetric ultrasound

VP18.01：机器学习应用于基础产科超声胎盘定位的标准化六步方法

• DOI: 10.1002/uog.24297
• 发表时间: 2021
• 期刊: Ultrasound in Obstetrics & Gynecology
• 影响因子: 7.1
• 作者: Self A