Lu Pinya, Lin Xiaolu, Liu Xiaofeng, Chen Mingfeng, Li Caiyan, Yang Hongqin, Wang Yuhua, Ding Xuemei
Fujian Provincial Engineering Research Centre for Public Service Big Data Mining and Application, Fujian Provincial University Engineering Research Center for Big Data Analysis and Application, Fujian Normal University, Fuzhou, China.
Department of Radiology, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital, Fuzhou, China.
Front Aging Neurosci. 2025 Mar 3;17:1554834. doi: 10.3389/fnagi.2025.1554834. eCollection 2025.
Inadequate primary care infrastructure and training in China and misconceptions about aging lead to high mis-/under-diagnoses and serious time delays for dementia patients, imposing significant burdens on family members and medical carers.
A flowchart integrating rural and urban areas of China dementia care pathway is proposed, especially spotting the obstacles of mis/under-diagnoses and time delays that can be alleviated by data-driven computational strategies. Artificial intelligence (AI) and machine learning models built on dementia data are succinctly reviewed in terms of the roadmap of dementia care from home, community to hospital settings. Challenges and corresponding recommendations to clinical transformation are then reported from the viewpoint of diverse dementia data integrity and accessibility, as well as models' interpretability, reliability, and transparency.
Dementia cohort study along with developing a center-crossed dementia data platform in China should be strongly encouraged, also data should be publicly accessible where appropriate. Only be doing so can the challenges be overcome and can AI-enabled dementia research be enhanced, leading to an optimized pathway of dementia care in China. Future policy-guided cooperation between researchers and multi-stakeholders are urgently called for dementia 4E (early-screening, early-assessment, early-diagnosis, and early-intervention).
中国初级医疗保健基础设施和培训不足，以及对老龄化的误解，导致痴呆症患者的误诊/漏诊率高且诊断严重延迟，给家庭成员和医疗护理人员带来了巨大负担。
本文提出了一个整合中国城乡痴呆症护理路径的流程图，特别指出了误诊/漏诊和时间延迟的障碍，这些障碍可以通过数据驱动的计算策略来缓解。从家庭、社区到医院环境的痴呆症护理路线图的角度，简要回顾了基于痴呆症数据构建的人工智能（AI）和机器学习模型。然后从痴呆症数据的完整性和可及性、模型的可解释性、可靠性和透明度等不同角度，报告了临床转化面临的挑战及相应建议。
应大力鼓励在中国开展痴呆症队列研究，并建立一个跨中心的痴呆症数据平台，在适当情况下数据应公开可获取。只有这样，才能克服挑战，加强人工智能辅助的痴呆症研究，从而优化中国的痴呆症护理路径。迫切需要研究人员与多利益相关者之间开展未来政策引导的合作，以实现痴呆症的4E（早期筛查、早期评估、早期诊断和早期干预）。
