Department of Health and Human Physiology, University of Iowa, Iowa City, IA, 52242, USA.
Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA, 52242, USA.
Neurol Sci. 2024 Nov;45(11):5117-5127. doi: 10.1007/s10072-024-07649-8. Epub 2024 Jun 13.
The objectives of this narrative review are to summarize the current state of AI applications in neuroimaging for early Alzheimer's disease (AD) prediction and to highlight the potential of AI techniques in improving early AD diagnosis, prognosis, and management.
We conducted a narrative review of studies using AI techniques applied to neuroimaging data for early AD prediction. We examined single-modality studies using structural MRI and PET imaging, as well as multi-modality studies integrating multiple neuroimaging techniques and biomarkers. Furthermore, they reviewed longitudinal studies that model AD progression and identify individuals at risk of rapid decline.
Single-modality studies using structural MRI and PET imaging have demonstrated high accuracy in classifying AD and predicting progression from mild cognitive impairment (MCI) to AD. Multi-modality studies, integrating multiple neuroimaging techniques and biomarkers, have shown improved performance and robustness compared to single-modality approaches. Longitudinal studies have highlighted the value of AI in modeling AD progression and identifying individuals at risk of rapid decline. However, challenges remain in data standardization, model interpretability, generalizability, clinical integration, and ethical considerations.
AI techniques applied to neuroimaging data have the potential to improve early AD diagnosis, prognosis, and management. Addressing challenges related to data standardization, model interpretability, generalizability, clinical integration, and ethical considerations is crucial for realizing the full potential of AI in AD research and clinical practice. Collaborative efforts among researchers, clinicians, and regulatory agencies are needed to develop reliable, robust, and ethical AI tools that can benefit AD patients and society.
本叙述性综述的目的是总结人工智能（AI）在神经影像学中用于早期阿尔茨海默病（AD）预测的应用现状，并强调 AI 技术在改善早期 AD 诊断、预后和管理方面的潜力。
我们对使用 AI 技术应用于神经影像学数据进行早期 AD 预测的研究进行了叙述性综述。我们检查了使用结构 MRI 和 PET 成像的单模态研究，以及整合多种神经影像学技术和生物标志物的多模态研究。此外，我们还回顾了对 AD 进展进行建模并识别有快速衰退风险的个体的纵向研究。
使用结构 MRI 和 PET 成像的单模态研究已经证明了在分类 AD 和预测从轻度认知障碍（MCI）到 AD 的进展方面具有很高的准确性。整合多种神经影像学技术和生物标志物的多模态研究与单模态方法相比，显示出了更好的性能和稳健性。纵向研究强调了 AI 在对 AD 进展进行建模和识别有快速衰退风险的个体方面的价值。然而，在数据标准化、模型可解释性、通用性、临床整合和伦理考虑方面仍然存在挑战。
应用于神经影像学数据的 AI 技术有可能改善早期 AD 的诊断、预后和管理。解决与数据标准化、模型可解释性、通用性、临床整合和伦理考虑相关的挑战对于充分发挥 AI 在 AD 研究和临床实践中的潜力至关重要。研究人员、临床医生和监管机构需要共同努力，开发可靠、稳健和符合伦理的 AI 工具，以造福 AD 患者和社会。
