人工智能诊断神经系统变性疾病——多模态影像与数字生物标志物在阿尔茨海默病与帕金森病早期筛选及预测中的最新进展OA

Neurodegenerative diseases,particularly Alzheimer disease(AD)and Parkinson disease(PD),are prominent and urgent public health problems in the context of global population aging.With the rapid development of artifi-cial intelligence(AI)technologies in recent years,the integration of multimodal imaging analysis and digital biomarkers has led to important breakthroughs in the early screening,accurate diagnosis,and progression prediction of these two dis-eases.Therefore,this paper systematically reviews the latest research advances in the application of AI to the diagnosis of neurodegenerative diseases,including multimodal image fusion technologies,digital biomarker extraction methods,and the latest innovations in deep learning algorithms,as well as various challenges encountered in clinical translation.This paper provides a detailed analysis of cutting-edge technologies such as machine learning models based on blood biomark-ers,deep learning architectures,smartphone application-based assessments,and monitoring with wearable devices,con-firming their potential value in the early detection of AD and PD.On this basis,the paper demonstrates the superiority of multimodal AI models in the diagnosis of AD and PD,discusses the prediction models for the conversion from mild cogni-tive impairment to AD,and outlines future research directions.This paper provides certain technical guidance and future development directions for AI-aided diagnosis of neurodegenerative diseases.