Ranson Janice M, Bucholc Magda, Lyall Donald, Newby Danielle, Winchester Laura, Oxtoby Neil P, Veldsman Michele, Rittman Timothy, Marzi Sarah, Skene Nathan, Al Khleifat Ahmad, Foote Isabelle F, Orgeta Vasiliki, Kormilitzin Andrey, Lourida Ilianna, Llewellyn David J
University of Exeter Medical School, College House, St Luke's Campus, Heavitree Road, Exeter, EX1 2LU, UK.
Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK.
Brain Inform. 2023 Feb 24;10(1):6. doi: 10.1186/s40708-022-00183-3.
Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock current challenges and advance precision dementia medicine.
痴呆症研究进展有限，在预防靶点、疾病进展机制和疾病修饰治疗等方面，我们的认知存在重大差距。多模态数据集日益丰富，为应用机器学习和人工智能（AI）来回答该领域的关键问题带来了可能性。我们概述了科学现状，强调了利用人工智能方法推动该领域在遗传学、实验医学、药物发现与试验优化、成像以及预防等领域发展的当前挑战与机遇。机器学习方法可以提高基因研究的结果，帮助确定生物学效应，并基于基因和转录组信息促进药物靶点的识别。使用无监督学习来理解药物发现的疾病机制很有前景，同时分析多模态数据集以表征和量化疾病严重程度及亚型也开始有助于优化临床试验招募。需要数据驱动的实验医学来跨模态分析数据，并开发新算法，将动物模型的见解转化为人类疾病生物学。神经成像中的人工智能方法在诊断分类方面优于传统方法，尽管在验证和转化方面仍存在挑战，但人们对其在不久的将来有意义地整合到临床实践持乐观态度。基于人工智能的模型还可以阐明我们对痴呆症风险因素因果关系和共性的理解，为风险预测模型提供信息并加以改进，同时推动预防性干预措施的发展。痴呆症的复杂性和异质性需要一种超越传统设计和分析方法的替代方法。尽管机器学习和人工智能在痴呆症研究中尚未广泛应用，但它们有潜力解决当前的挑战并推动精准痴呆症医学的发展。
