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.

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利用机器学习和人工智能的潜力进行痴呆症研究

痴呆症研究的进展有限，我们在预防目标、疾病进展机制和疾病缓解治疗方面的知识存在很大差距。多模式数据集的可用性不断增加，为机器学习和人工智能 (AI) 的应用打开了可能性，以帮助回答该领域的关键问题。我们概述了科学现状，强调了当前利用人工智能方法推动遗传学、实验医学、药物发现和试验优化、成像和预防领域向前发展的挑战和机遇。机器学习方法可以增强遗传研究的结果，帮助确定生物效应，并促进基于遗传和转录组信息的药物靶标的识别。使用无监督学习来理解药物发现的疾病机制是有希望的，同时分析多模态数据集来表征和量化疾病严重程度和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分析跨模式的数据并开发新的算法以将动物模型的见解转化为人类疾病生物学。神经影像学中的 AI 方法优于传统的诊断分类方法，尽管围绕验证和翻译的挑战仍然存在，但人们对它们在不久的将来有意义地融入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的因果关系和共性的理解，通知和改进风险预测模型以及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。尽管尚未广泛用于痴呆症研究，但机器学习和人工智能有潜力解决当前的挑战并推进精准痴呆症医学。