AI-powered next-generation imaging biomarkers for dementia

人工智能驱动的下一代痴呆症成像生物标志物

• 批准号: MR/W004097/1
• 负责人: Hui Zhang
• 金额: $ 9.89万
• 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: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FW004097%2F1
• 关键词: AI; powered; next; generation; imaging

Dementia is one of the foremost global healthcare challenges of our times, with a vast physical, psychological, social and economic impact. About 50 million people worldwide live with dementia today and this number is expected to triple by 2050. The direct medical and social care costs have been estimated in 2015 to be 1.4% of gross domestic product in high-income countries. Since 2016, dementia has become the leading cause of deaths in the UK. Even today, in the midst of an unprecedented pandemic, Covid-19 is hitting people with dementia the hardest, with dementia being the most common pre-existing condition for people dying of Covid-19. Moreover, patients recovered from Covid-19 are thought to be at significantly increased risk for dementia later in life. Indeed, the need to find a cure for dementia has never been greater.However, till this day, no disease-modifying treatments have been developed. The attrition rate of treatment trials for Alzheimer's disease (AD), the commonest cause of dementia, is high, with 98% failing in Phase III. The reason, at least in part, for the successive, and often costly, trial failures is likely to be that the disease is targeted too late, after significant and irreversible loss of brain cells has occurred. Hence, there is now a consensus and drive towards undertaking trials of therapies much earlier, before the disease becomes irreversible. This calls for the development of more advanced biomarkers that can offer increased sensitivity for identifying individuals at early stages of the disease.University College London (UCL) is an international leader in the development of cutting-edge MRI biomarkers for early diagnosis of dementia. Dr Michele Guerreri, the proposed secondee, is an early-career imaging researcher at UCL who has made significant progress in our quest towards better biomarkers for dementia. This secondment is driven by the aspiration to translate this cutting-edge research into a product to accelerate its real-world impact.AINOSTICS, the hosting organization, is an award-winning imaging AI start-up which specialises in combining cutting-edge MRI and other biomarker data with state-of-the-art artificial intelligence (AI) to detect early changes in AD and other neurodegenerative diseases. They thus represent a perfect partner to deliver this project.This project will boost the skills of Dr Guerreri which will be working in a cutting-edge AI environment, learning the translation process from a research idea into a commercial product. The Secondee will have the opportunity to experience the commercial sector for the first time, learning the development and commercialisation process of his academic research. The hosting organisation will benefit from the Secondee's expertise in the development of state-of-the-art imaging biomarkers for dementia.In the long term, the project will lay the groundwork for a close collaboration between UCL and AINOSTICS. The expertise gained by Dr Guerreri will allow him to develop similar relationships with other players in the vibrant UK imaging AI sector, closing the traditional gap that has been preventing or slowing the knowledge exchange between biomedical industry and academia.

痴呆症是我们时代最重要的全球医疗保健挑战之一，具有巨大的身体，心理，社会和经济影响。当今，全世界约有5000万人生活在痴呆症中，到2050年，这一数字预计将三倍。直接的医疗和社会护理费用估计在2015年估计为高收入国家的国内生产总值的1.4％。自2016年以来，痴呆症已成为英国死亡的主要原因。即使在今天，在一个前所未有的大流行中，Covid-19仍在击中痴呆症患者最困难的人，痴呆症是Covid-19垂死的人们最常见的先前疾病。此外，从COVID-19中恢复的患者被认为是生命后期痴呆症的风险显着增加。的确，寻找治愈痴呆症的需求从未如此大。直到今天，尚未开发出疾病的治疗方法。阿尔茨海默氏病（AD）的治疗试验的流失率是痴呆症最常见的原因，在第三阶段失败了98％。至少部分原因是连续且通常昂贵的原因，试验失败可能是由于发生了严重且不可逆转的脑细胞丧失后，该疾病的目标太晚了。因此，在这种疾病变得不可逆转之前，现在已经达成共识，并开车早些时候对疗法进行试验。这呼吁开发更先进的生物标志物，这些标志物可以在疾病早期阶段识别个人的敏感性。拟议中的二等赛米歇尔·格雷里（Michele Guerreri）博士是UCL的早期成像研究员，他在寻求更好的痴呆症生物标志物方面取得了重大进展。 This secondment is driven by the aspiration to translate this cutting-edge research into a product to accelerate its real-world impact.AINOSTICS, the hosting organization, is an award-winning imaging AI start-up which specialises in combining cutting-edge MRI and other biomarker data with state-of-the-art artificial intelligence (AI) to detect early changes in AD and other neurodegenerative diseases.因此，它们代表了交付该项目的理想合作伙伴。该项目将提高Guerreri博士的技能，该项目将在尖端的AI环境中工作，学习从研究思想到商业产品的翻译过程。第二天将有机会第一次体验商业部门，学习他的学术研究的发展和商业化过程。托管组织将受益于第二次痴呆症的最先进成像生物标志物的专业知识。从长远来看，该项目将为UCL与Ainottics之间的密切合作奠定基础。 Guerreri博士获得的专业知识将使他能够与充满活力的英国成像AI部门中的其他参与者建立相似的关系，从而弥补了一直在阻止生物医学行业和学术界之间知识交流的传统差距。

项目成果

Deep learning framework for accelerating revised-NODDI parameter estimation with tensor-valued diffusion encoding

使用张量值扩散编码加速修订版 NODDI 参数估计的深度学习框架

• 发表时间: 2022
• 作者: Guerreri M.

Revised-NODDI with conventional dMRI data enabled by deep learning

深度学习支持的传统 dMRI 数据的修订版 NODDI

• 发表时间: 2022
• 作者: Guerreri M.

Resolving quantitative MRI model degeneracy with machine learning via training data distribution design

通过训练数据分布设计利用机器学习解决定量 MRI 模型简并性

• 发表时间: 2023
• 作者: Guerreri M

Information Processing in Medical Imaging - 28th International Conference, IPMI 2023, San Carlos de Bariloche, Argentina, June 18-23, 2023, Proceedings

医学影像信息处理 - 第 28 届国际会议，IPMI 2023，阿根廷圣卡洛斯-德巴里洛切，2023 年 6 月 18-23 日，会议记录

• DOI: 10.1007/978-3-031-34048-2_1
• 发表时间: 2023
• 作者: Guerreri M

Can machine learning resolve model degeneracy in tissue microstructure estimation?

机器学习能否解决组织微观结构估计中的模型简并性？

• 发表时间: 2023
• 作者: Michele Guerreri