Clinical adoption of quantitative neuro-imaging AI based techniques

基于定量神经成像人工智能技术的临床采用

• 批准号: 2876046
• 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: 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-2876046
• 关键词: Clinical; adoption; quantitative; neuro; imaging

1) Brief description of the context of the research including potential impactNumerous advancements have been made in recent years regarding the use of artificial intelligence (AI) in medical imaging. Nonetheless, very few AI tools make it into clinical practice. The primary goal of the current PhD project is to investigate key sources of error and develop uncertainty measures for quantitative neuroimaging AI tools. The focus will be on neuro-oncology, dementia, epilepsy and MS. Potential impacts of the project include the facilitation of clinical adoption of AI assisted volumetric analysis, AI enhanced acquisition times and the translation of uncertainty measures associated with quantitative MR measurements into the clinic. 2) Aims and ObjectivesAims: Development of methodology that will enhance clinical translation of AI based tools. Objective: 1. Identification of key sources of errors in existing AI tools.2. Development and validation of uncertainty measures for automated volumetric analyses.3. Evaluation of the implications of using AI technologies in the speed up of acquisitions in neuroradiology.3) Novelty of Research MethodologyAs of now, many AI tools in the field of neuroimaging do not convey their degree of uncertainty concomitant to the prediction. Furthermore, it is not sufficiently well understood what the impact is of using AI to speed up the acquisition of MR images. 4) Alignment to EPSRC's strategies and research areasThis project aligns with the EPSRC's research aims within healthcare technologies. Automated extraction of information from clinical data or images is a high priority research area of the EPSRC's research on medical imaging. 5) Any companies or collaborators involvedCurrently no companies or external collaborators involved.

1)简要描述研究的背景，包括潜在的影响近年来在医学成像中使用人工智能(AI)取得了大量进展。尽管如此，很少有人工智能工具进入临床实践。目前博士项目的主要目标是调查误差的关键来源，并为定量神经成像AI工具开发不确定性测量。重点将放在神经肿瘤学、痴呆症、癫痫和多发性硬化症上。该项目的潜在影响包括促进临床采用人工智能辅助容量分析、人工智能增强的采集时间以及将与定量磁共振测量相关的不确定性测量转化为临床。2)目的和目的目的：发展方法学，以加强基于人工智能的工具的临床翻译。目标：1.识别现有人工智能工具中的主要错误来源。开发和验证自动化容量分析的不确定度测量。3)研究方法的新颖性到目前为止，神经成像领域的许多人工智能工具都没有传达出与预测相关的不确定性程度。此外，人们还没有充分了解使用人工智能来加快MR图像获取的影响。4)与EPSRC的战略和研究领域保持一致本项目与EPSRC在医疗保健技术领域的研究目标保持一致。从临床数据或图像中自动提取信息是EPSRC医学成像研究的一个高度优先的研究领域。5)是否涉及公司或合作者目前未涉及任何公司或外部合作者。