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Endoscopy event recognition using machine learning

使用机器学习进行内窥镜事件识别

基本信息

批准号：
2405083
负责人：
金额：
--
依托单位：
University College London
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2020
资助国家：
英国
起止时间：
2020 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2405083
关键词：
Endoscopy event recognition using machine

项目摘要

1) Brief description of the context of the research including potential impactAn endoscopic procedure such as colonoscopy is defined by a series of events these include reaching thecaecum, withdrawal, detecting/diagnosing disease, resection and removal. The quality of a procedure ismeasured by metrics related to these events e.g. % of procedures where caecum is reached, number ofpolyps detected etc. There are multiple studies that show the quality of colonoscopy varies significantlyamongst doctors and that the recording and identification of these events is inconsistent.The scope of this project is to explore machine learning to improve the detection and documentation of events in colonoscopy with the goal of improving the quality of procedures. 2) Aims and ObjectivesThe specific objectives are to:Develop video-based methods that include temporal information for event detection. 3) Novelty of Research MethodologyThis is a highly challenging topic that will address open research questions related to temporal machine learning with small amounts of datadomain adaption for data generalization and multi-task learning that can use data from other endoscopic events to improve performance.4) Alignment to EPSRC's strategies and research areasThis project lies within the healthcare technology theme helping to accelerate translation within the scope of the following grand challenges:Developing future therapiesFrontiers of physical interventionOptimising treatmentTransforming community health and care5) Any companies or collaborators involvedOdin Medical Limited

1)研究背景的简要描述，包括潜在影响内镜手术如结肠镜检查是由一系列事件定义的，这些事件包括到达盲肠，撤离，检测/诊断疾病，切除和切除。手术的质量是通过与这些事件相关的指标来衡量的，例如手术到达盲肠的百分比，检测到的息肉数量等。有多项研究表明，不同医生的结肠镜检查质量差异很大，记录和识别这些事件也不一致。该项目的范围是探索机器学习，以提高结肠镜检查事件的检测和记录，目的是提高手术质量。2)目标和目标具体目标是：开发基于视频的方法，包括事件检测的时间信息。3)研究方法的新颖性这是一个极具挑战性的主题，将解决与时间机器学习相关的开放式研究问题，使用少量数据域自适应进行数据泛化和多任务学习，可以使用来自其他内窥镜事件的数据来提高性能。4)与EPSRC的战略和研究领域保持一致该项目属于医疗技术主题，有助于在以下重大挑战的范围内加速翻译：开发未来疗法，物理干预的前沿，优化治疗，改变社区健康和护理5)任何公司或合作者参与dodin医疗有限公司