Machine learning to classify periportal fibrosis from point-of-care ultrasound to develop clinical decision support systems for schistosomiasis in sub

机器学习根据护理点超声对门静脉周围纤维化进行分类，以开发亚种血吸虫病的临床决策支持系统

• 批准号: 2593890
• 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: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2593890
• 关键词: Machine; learning; classify; periportal; fibrosis

Schistosomiasis is an infectious disease that is most prevalent in sub-Saharan Africa due to poor access to potable water and sanitation. Over 700 million people are at risk. A parasitic blood fluke causes the conditions related to schistosomiasis. For intestinal schistosomiasis (such as that caused by Schistosoma mansoni), these conditions can include diarrhoea, blood in stool, abdominal distention, gut inflammation, anaemia, enlarged spleens/livers, and in the most severe form periportal fibrosis and portal hypertension. Periportal fibrosis is caused when schistosome eggs are lodged in the portal veins and new branches form to sustain the blood supply to the liver. The mainstay of periportal fibrosis diagnosis is via manual acquisition and grading of liver fibrotic patterns in ultrasound imaging using the World Health Organisation (WHO) Niamey Protocol. This protocol is complex, lengthy, and subject to high inter-reader variability. Advanced expertise in sonography is needed to arrive at the periportal fibrosis grades identified in the Niamey Protocol. In rural poor settings where periportal fibrosis is most prevalent, there is a limited availability of sonographers. The Niamey Protocol was developed for human use before the wide availability of ultrasound devices with the capacity to save images and videos. No updates have been made to the Niamey protocol since its inception in 1996. There is an urgent need to develop automated analyses for periportal fibrosis and investigate whether new features can be detected that are relevant for schistosomal liver disease.To solve this problem, an automated analysis pipeline will be developed that will take an ultrasound video generated from a specific set of simple-to-acquire sweeps of the liver. The main goals will be to produce assistive aids for identifying where fibrosis occurs, produce an automated version of the grading of liver fibrosis according to the Niamey Protocol, and to compare new features/classifications detected to the existing grading procedure. The first step will be to identify the video frames with the best representation of different anatomies in the video, using automated segmentation with supervised and unsupervised approaches. Next, a classifier will be developed to look for features in these images that indicate different grades of fibrosis. The dataset to be analysed will come from SchistoTrack, which is a human participant cohort that was set up to investigate liver fibrosis progression in rural populations in Western and Eastern Uganda.Aims1. To develop a pipeline for automated liver segmentation, identifying relevant anatomy for periportal fibrosis.2. To identify features of fibrosis grades from ultrasound videos and images focused on liver anatomies with validation from a trained sonographer to promote model interpretability and trustworthiness.3. To test the performance of the automated analysis pipeline by identifying robust evaluation metrics apart from accuracy.4. To analyse data from different sites/groupings and consider how these fit into clinical practice in Uganda, including a pilot test in the study areas.This research will make progress in automated abdominal ultrasound segmentation and fibrotic feature detection in the livers. These machine learning-based topics are poorly understood. The main goal is for the analysis and assistive technology pipelines to be accurate, flexible and interpretable, meaning they could be used to aid and train sonographers in sub-Saharan Africa and to contribute to addressing the clinical need for quicker diagnosis and intervention.This project falls within the EPSRC's healthcare technologies theme. It is a collaboration with the Uganda Ministry of Health through the Oxford-Uganda Collaboration on Schistosomiasis and the SchistoTrack human participant cohort.

血吸虫病是一种传染病，由于缺乏饮用水和卫生设施，在撒哈拉以南非洲最为流行。超过7亿人处于危险之中。寄生血吸虫引起血吸虫病相关的条件。对于肠血吸虫病（例如由曼氏血吸虫引起的肠血吸虫病），这些病症可以包括腹泻、便血、腹胀、肠道炎症、贫血、脾/肝肿大，以及最严重的形式的门静脉周围纤维化和门静脉高压症。门静脉周围纤维化是由寄生虫卵停留在门静脉中并形成新的分支以维持肝脏的血液供应而引起的。门静脉周围纤维化诊断的主要方法是使用世界卫生组织（WHO）尼亚美方案，通过超声成像手动采集和分级肝纤维化模式。该方案复杂、冗长，并且受阅片者之间高度可变性的影响。需要超声检查的高级专业知识来达到尼亚美方案中确定的门静脉周围纤维化等级。在农村贫困地区，门静脉周围纤维化最普遍，超声医师的可用性有限。尼亚美协议是在具有保存图像和视频能力的超声设备广泛可用之前为人类使用而开发的。尼亚美议定书自1996年开始实施以来，未作任何更新。目前迫切需要开发门静脉周围纤维化的自动化分析，并研究是否可以检测到与染色体性肝病相关的新特征。为了解决这个问题，将开发一个自动化分析管道，该管道将从一组特定的简单采集的肝脏扫描中生成超声视频。主要目标是制作用于识别纤维化发生位置的辅助工具，根据尼亚美方案制作肝纤维化分级的自动化版本，并将检测到的新特征/分类与现有分级程序进行比较。第一步将是使用有监督和无监督方法的自动分割来识别具有视频中不同解剖结构的最佳表示的视频帧。接下来，将开发一个分类器来寻找这些图像中指示不同级别纤维化的特征。待分析的数据集将来自SchistoTrack，这是一个人类参与者队列，旨在调查乌干达西部和东部农村人群的肝纤维化进展。开发自动肝脏分割的管道，识别门静脉周围纤维化的相关解剖结构。从聚焦于肝脏解剖结构的超声视频和图像中识别纤维化等级的特征，并由经过培训的超声医师进行验证，以提高模型的可解释性和可信度。通过识别除准确性外的稳健评估指标来测试自动化分析管道的性能。分析来自不同地点/分组的数据，并考虑这些数据如何适合乌干达的临床实践，包括在研究领域进行试点测试。这项研究将在自动腹部超声分割和肝脏纤维化特征检测方面取得进展。这些基于机器学习的主题人们知之甚少。主要目标是使分析和辅助技术管道准确、灵活和可解释，这意味着它们可以用于帮助和培训撒哈拉以南非洲的超声医师，并有助于解决更快诊断和干预的临床需求。该项目福尔斯属于EPSRC的医疗技术主题。它是通过牛津-乌干达血吸虫病合作和血吸虫病追踪人类参与者队列与乌干达卫生部合作的。