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) 来训练算法来识别治疗和对照大鼠肺组织红外光谱图像中的异常区域？我们能否开发一种标准化算法，适用于从任何对照和治疗大鼠采集的红外光谱图像我们可以使用红外光谱图像的数据和经过训练的算法来识别在 24 小时吸入研究中从大鼠身上采集的肺部不良呼吸系统结果吗？使用开发的算法更早地检测到治疗肺组织中的不良结果是否会使先正达能够进行更少的动物研究或在早期阶段终止不成功的研究？该项目将继续之前的博士项目的工作，开发一种用于检测不良结果的算法对照和治疗肺组织的研究结果取自最近 28 天的大鼠/小鼠农用化学品吸入研究。虽然组织组成和成分会有所不同，但小鼠肝脏研究中开发的分析和建模技术很容易转移到肺部，并且样品制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群和病变类型有效，则可能会导致标准化化学病变检测算法的开发，并在未来的病理工作流程中实施。这主要是一个计算和人工智能项目，旨在以以前的博士生的工作为基础。该项目将探索新方法和新网络架构，使我们能够从每个样本中提取最大量的信息。新颖的科学将在于算法开发。这是 EPSRC 的职权范围，预计学生将具有计算科学/人工智能背景