I-AIM: Individualised Artificial Intelligence for Medicine

I-AIM：个性化医学人工智能

• 批准号: MR/S03546X/1
• 负责人: Neil Oxtoby
• 金额: $ 106.83万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS03546X%2F1
• 关键词: AIM; Individualised; Artificial; Intelligence; Medicine

Management and treatment of complex, chronic diseases such as Alzheimer's disease is one of the biggest challenges facing modern medicine. All clinical trials of investigational treatments for slowing or stopping the progression of Alzheimer's disease since 2003 have failed. This is likely due to the complexity and duration (decades) of Alzheimer's disease, coupled with the highly individual nature of the disease and its progression. Combined, this works against clinical trials by making it extremely difficult to identify and recruit a large group of individuals who are at the same stage of the same trajectory, and so who might benefit from a potential treatment. In principle, this challenge can be met by a set of modern computational approaches called data-driven disease progression modelling (D3PM), but some technological development is required first.D3PM aims to combine statistics with the latest developments in AI and data science to estimate disease signatures that describe how a progressive disease plays out from beginning to end. This active research field grew from basic supervised machine learning (pattern learning/recognition) to a range of phenomenological (top-down) models, and mechanistic (bottom-up) models that incorporate a range of AI tools including unsupervised machine learning (pattern discovery). D3PM signatures have shown promise for estimating severity and predicting progression in neurodegenerative diseases such as Alzheimer's disease, but they currently lack in individual level precision, and mechanistic rigour.This research and innovation project is a unique combination of technology development and translational product development: a series of novel technological developments for individualising D3PM and expanding mechanistic modelling; and translational efforts to develop drug-development tools based on this next-generation technology. In combination, this work will speed up drug-development by increasing the efficiency of clinical trials: recruiting smaller cohorts of suitable individuals will reduce costs and lead to fewer false-negative results - where a drug works on a fraction of the population, but the trial cannot detect it because the majority did not respond to treatment. The chosen application is Alzheimer's disease, but the ideas are fit-for-purpose for similarly complex, progressive diseases.This fellowship is a significant launchpad for my career. My ambition is to benefit patients and society by providing robust computational solutions to complex healthcare challenges. My vision for achieving this ambition starts by targeting the global epidemic of dementia, where I have identified an unmet need (improving clinical trials) and proposed a viable solution in the form of this research and innovation project. The fellowship provides essential resources to capitalise on my recent progress in the field and to personally develop into a UK-based future leader in using AI for medicine and health.

管理和治疗阿尔茨海默病等复杂的慢性疾病是现代医学面临的最大挑战之一。自2003年以来，所有延缓或阻止阿尔茨海默病进展的研究性治疗的临床试验都失败了。这可能是由于阿尔茨海默病的复杂性和持续时间(几十年)，加上疾病及其进展的高度个人化的性质。总而言之，这不利于临床试验，因为它使得识别和招募一大群处于相同轨迹的相同阶段的人变得极其困难，因此谁可能从潜在的治疗中受益。原则上，这一挑战可以通过一套名为数据驱动的疾病进展建模(D3PM)的现代计算方法来应对，但首先需要一些技术开发。D3PM的目标是将统计学与人工智能和数据科学的最新发展相结合，以估计描述疾病从头到尾如何发展的疾病特征。这一活跃的研究领域从基本的监督机器学习(模式学习/识别)发展到一系列现象学(自上而下)模型，以及结合了一系列人工智能工具的机械论(自下而上)模型，包括无监督机器学习(模式发现)。D3 PM签名在估计阿尔茨海默病等神经退行性疾病的严重程度和预测进展方面显示出了希望，但目前它们缺乏个人水平的精度和机制的严谨性。这一研究和创新项目是技术开发和翻译产品开发的独特组合：一系列用于个性化D3 PM和扩展机制建模的新技术开发；以及基于这一下一代技术开发药物开发工具的翻译努力。结合起来，这项工作将通过提高临床试验的效率来加快药物开发：招募更小的合适个体队列将降低成本，并导致更少的假阴性结果--即一种药物对一小部分人群有效，但试验无法检测到它，因为大多数人对治疗没有反应。选择的应用程序是阿尔茨海默氏症，但这些想法适用于同样复杂的进行性疾病。这一奖学金是我职业生涯的一个重要发射台。我的抱负是通过为复杂的医疗挑战提供强大的计算解决方案来造福患者和社会。我实现这一雄心壮志的愿景始于瞄准全球痴呆症流行，在那里我确定了一个未得到满足的需求(改善临床试验)，并以这一研究和创新项目的形式提出了一个可行的解决方案。该奖学金提供了必要的资源，以利用我在该领域的最新进展，并亲自发展成为一名在医学和健康领域使用人工智能的英国未来领导者。

项目成果

Tau-first subtype of Alzheimer's disease progression consistently identified through PET and CSF Neuroimaging: Understanding tau progression

通过 PET 和 CSF 神经影像一致鉴定出阿尔茨海默病进展的 Tau 第一个亚型：了解 tau 进展

• DOI: 10.1002/alz.045412
• 发表时间: 2020
• 期刊: Alzheimer's & Dementia
• 作者: Aksman L

The sequence of structural, functional and cognitive changes in multiple sclerosis.

• DOI: 10.1016/j.nicl.2020.102550
• 发表时间: 2021
• 期刊: NeuroImage. Clinical
• 作者: Dekker I;Schoonheim MM;Venkatraghavan V;Eijlers AJC;Brouwer I;Bron EE;Klein S;Wattjes MP;Wink AM;Geurts JJG;Uitdehaag BMJ;Oxtoby NP;Alexander DC;Vrenken H;Killestein J;Barkhof F;Wottschel V
• 通讯作者: Wottschel V

Neurodegenerative disease of the brain: a survey of interdisciplinary approaches.

• DOI: 10.1098/rsif.2022.0406
• 发表时间: 2023-01
• 期刊: Journal of the Royal Society, Interface

Inter-cohort staging efficacy of gaussian process progression model for Alzheimer's disease Neuroimaging / Optimal neuroimaging measures for tracking disease progression

阿尔茨海默病高斯过程进展模型的队列间分期功效神经影像学/跟踪疾病进展的最佳神经影像学措施

• DOI: 10.1002/alz.043246
• 发表时间: 2020
• 期刊: Alzheimer's & Dementia
• 作者: Archetti D

Analyzing large Alzheimer's disease cognitive datasets: Considerations and challenges.

• DOI: 10.1002/dad2.12135
• 发表时间: 2020
• 期刊: Alzheimer's & dementia (Amsterdam, Netherlands)
• 作者: Bellio M;Oxtoby NP;Walker Z;Henley S;Ribbens A;Blandford A;Alexander DC;Yong KXX
• 通讯作者: Yong KXX