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Distributed Intelligent Learning Environment for Mammographic Screening

乳腺X线筛查分布式智能学习环境

基本信息

批准号：
EP/E033490/1
负责人：
Paul Taylor
金额：
$ 40.21万
依托单位：
University College London
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2007
资助国家：
英国
起止时间：
2007 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=EP%2FE033490%2F1
关键词：
Distributed Intelligent Learning Environment Mammographic

项目摘要

Breast cancer is one of the major causes of death in the modern world. In the UK there is a national screening programme which women between ages of 50 and 70 can attend. Breast cancer screening involves taking breast X-Rays (called mammograms) and examining them for signs of cancer. The idea is that if cancers are detected and treated early (before there are noticeable symptoms) then treatments can be more effective. Examining mammograms for cancer is a highly skilled job carried out by trained radiologists who have to detect what are often very subtle abnormalities occurring only in a small proportion of the cases they examine. Our research will explore how computers can be effectively used to train radiologists to undertake the demanding task of breast screening. To do this we will develop and test an Intelligent Tutoring and e-Learning Environment (ITeLE) to provide instruction, support, practice and feedback for trainee radiologists intending to specialize in mammography.Although computer-based training tools have been developed for branches of radiology other than mammography, few have progressed to widespread and routine use. This is because the development of successful computer-based training systems presents us with a number of problems of different kinds, requiring different approaches and methods for their solution. To develop the ITeLE, we propose to use an interdisciplinary approach that draws upon and brings together insights from psychology, sociology and computer science, in the following ways:Cognitive psychology is concerned with how humans process information, and tells us how radiologists with different levels of skill approach the problem of interpreting medical images. Previous work has shown how, for example, novice radiologists are more likely to interpret images by applying rules that describe the difference between normal and abnormal features in an image. As they gain in experience, however, they come to rely more on matching features in the image in front of them with their memory of the many examples of similar features seen during their career. Psychology, then, gives us clues as to the sorts of training might be most appropriate as trainees' experience increases, which can be incorporated into an intelligent tutoring tool: initially tutorials to teach the rules of interpretation, followed by exercises giving trainees practice at distinguishing normal and abnormal presentations, progressing finally to simulating screening conditions where trainees would have to spot a variety of abnormal cases 'hidden' amongst normal ones.e-Learning environments need to be designed with an understanding of the work practices and expertise that they aim to support. We will draw upon the methods of sociology to understand the practical details of radiology training. By observing the work of training and the circumstances in which it takes place, and by involving trainees and mentors closely in the design and development of the ITeLE, we aim to produce an e-Learning environment that closely matches their needs, and which they find easy to understand and use. In this way, we can ensure that the tools we develop on the basis of our understandings of psychology are both useful and usable in practice.e-Learning environments make it possible to build a record of trainee decisions, including cases or features in the image they have struggled to identify correctly. Using methods from artificial intelligence (a branch of computer science), we intend to explore how this information can be used to automatically produce feedback (for example, indicating where a trainee's strengths and weaknesses lie) and advice (for example, concerning what tasks it would be appropriate for a trainee to tackle next). In the final stages of the project, we will undertake an evaluation of the ITeLE to demonstrate the effectiveness of the intelligent tutoring tool and of the different training strategies.

乳腺癌是现代世界的主要死亡原因之一。英国有一个全国性的筛查计划，50至70岁的妇女可以参加。乳腺癌筛查包括拍摄乳房X光片（称为乳房X光片）并检查它们是否有癌症的迹象。这个想法是，如果癌症被发现和治疗早期（之前有明显的症状），那么治疗可以更有效。检查乳房X光片中的癌症是一项高技能的工作，由训练有素的放射科医生进行，他们必须检测出通常只在他们检查的一小部分病例中发生的非常微妙的异常。我们的研究将探讨如何有效地利用计算机来培训放射科医生承担乳房筛查的艰巨任务。为此，我们将开发和测试智能辅导和电子学习环境（ITeLE），为打算专门从事乳腺X线摄影的实习放射科医生提供指导，支持，实践和反馈。尽管基于计算机的培训工具已被开发用于乳腺X线摄影以外的放射学分支，但很少有进展到广泛和常规使用。这是因为成功的基于计算机的培训系统的开发给我们带来了许多不同类型的问题，需要不同的方法和方法来解决。为了开发ITeLE，我们建议使用跨学科的方法，以以下方式借鉴并汇集心理学、社会学和计算机科学的见解：认知心理学关注人类如何处理信息，并告诉我们具有不同水平的放射科医生如何处理信息。技能解决解释医学图像的问题。以前的工作已经表明，例如，新手放射科医生更有可能通过应用描述图像中正常和异常特征之间差异的规则来解释图像。然而，随着经验的积累，他们越来越依赖于将面前图像中的特征与他们在职业生涯中看到的许多相似特征的例子相匹配。因此，心理学为我们提供了线索，告诉我们随着受训者经验的增加，哪种培训可能是最合适的，这可以纳入智能辅导工具：首先是讲授口译规则的教程，然后是练习，让学员练习区分正常和不正常的陈述，最后，模拟筛查条件，受训者必须发现各种“隐藏”在正常病例中的异常病例。学习环境的设计需要了解他们旨在支持的工作实践和专业知识。我们将利用社会学的方法来了解放射学培训的实际细节。通过观察培训工作及其发生的情况，并通过让学员和导师密切参与ITeLE的设计和开发，我们的目标是创造一个紧密匹配他们需求的电子学习环境，并且他们发现易于理解和使用。通过这种方式，我们可以确保我们根据对心理学的理解开发的工具在实践中既有用又可用。电子学习环境可以建立学员决策的记录，包括他们努力正确识别的图像中的案例或特征。使用人工智能（计算机科学的一个分支）的方法，我们打算探索如何使用这些信息来自动产生反馈（例如，指出受训者的强项和弱点）和建议（例如，关于受训者下一步应该处理什么任务）。在项目的最后阶段，我们将对ITeLE进行评估，以证明智能辅导工具和不同培训策略的有效性。