Infrared (IR) spectroscopic imaging and digital pathology for development of algorithm for early detection of test-article related lung findings in th

红外 (IR) 光谱成像和数字病理学用于开发算法，以便在实验室中早期检测与测试物品相关的肺部发现

• 批准号: 2903657
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2903657
• 关键词: Infrared; IR; spectroscopic; imaging; digital

Research questionsCan we apply deep learning (AI) to train an algorithm to recognize areas of abnormality in treated and control rat lung tissue infrared spectroscopic images?Can we develop a standardized algorithm that can be applied to infrared spectroscopic images taken from any control and treated rat tissue to enable recognition of adverse findings before histopathology lesions are apparent.Can we use the data from the infrared spectroscopic images and the algorithm trained to recognize adverse respiratory findings in lungs taken from rats on 24 hour inhalation studies. Will the earlier detection of adverse findings in treated lung tissue using the developed algorithm enable Syngenta to conduct fewer animal studies or terminate unsuccessful studies at an earlier stage?This project will continue work from a previous PhD project to develop an algorithm for the detection of adverse findings within control and treated lung tissue taken from recent 28 day inhalation studies with agrochemicals, in rats/mice. While the tissue makeup and constituents will differ, the analytical and modelling techniques developed in the mouse liver study are easily transferrable to lung and the sample preparation and data collection protocols remain unchanged. If these techniques can be proven to be effective across different tissue groups and lesion types, it could lead to the development of a standardised chemical lesion detection algorithm to be implemented in future pathology workflows. This is largely a computational and AI project which aims to build on work from a previous PhD student. This project will explore new methods and new network architectures that will enable us to extract the maximum amount of information from each sample. The novel science will be in the algorithm development. This is within the EPSRC remit and it is expected that the student will have a computational science / AI background

研究问题我们能否应用深度学习(AI)来训练一种算法，以识别处理和控制的大鼠肺组织红外光谱图像中的异常区域？我们是否可以开发一种标准化的算法，可以应用于从任何对照和治疗的大鼠组织获取的红外光谱图像，以便在组织病理学损伤明显之前识别不良发现。我们是否可以使用红外光谱图像的数据和训练的算法来识别24小时吸入试验中大鼠肺部的不良呼吸发现。使用开发的算法在治疗后的肺组织中更早地检测到不良发现是否使先正达能够进行更少的动物研究或在更早阶段终止不成功的研究？该项目将继续之前博士项目的工作，开发一种算法，用于检测大鼠/小鼠最近28天的农用化学品吸入试验中对照和治疗后肺组织的不良发现。虽然组织的组成和成分会有所不同，但在小鼠肝脏研究中开发的分析和建模技术很容易移植到肺部，而样本准备和数据收集方案保持不变。如果这些技术能够被证明对不同的组织组和病变类型有效，就可能导致开发一种标准化的化学病变检测算法，以便在未来的病理工作流程中实施。这在很大程度上是一个计算和人工智能项目，旨在建立在以前博士生工作的基础上。这个项目将探索新的方法和新的网络体系结构，使我们能够从每个样本中提取最大数量的信息。新的科学将出现在算法的开发中。这在EPSRC的职权范围内，预计该学生将具有计算科学/人工智能背景