Advances in gait research related to Alzheimer’s disease - Shuding Yan; Xiaoping Yun; Qiang Liu; Zhenmei Hong; Yufan Chen; Shuijing Zhang (Frontiers in Neurology)
IntroductionAlzheimer’s disease (AD) represents a degenerative condition affecting the nervous system, characterized by the absence of a definitive cause and a lack of a precise therapeutic intervention. Extensive research efforts are being conducted worldwide to enhance early detection methods for AD and to develop medications capable of effectively halting the initiation and progression of the disease during its early stages. Some current detection methods for early diagnosis are expensive and require invasive procedures. More and more evidence shows that gait is related to cognition. A deeper investigation into the intricate interplay between gait and cognition is necessary to elucidate their reciprocal influences and the temporal sequence of these interactions. In the future, it is hoped that with the results of clinical manifestations, neuroimaging, and electrophysiology, simple and objective gait analysis results can be used as an alternative biomarker for cognitive decline to diagnose dementia early.Research objectiveThis research offers a comprehensive scoping review of the contemporary landscape of clinical gait evaluation. It delineates the pertinent concepts of gait analysis and machine learning in AD and elucidates the intricate interplay between gait patterns and cognitive status.MethodsA comprehensive literature search was conducted within PubMed for all articles published until march 18, 2024, using a set of keywords, including “machine learning and gait “and “gait and Alzheimer.” original articles that met the selection criteria were included.Results and significanceA strong correlation exists between autonomous gait and cognitive attributes, necessitating further investigation into the selective interplay between gait and mental factors. Conversely, the gait information of Alzheimer’s disease (AD) patients can be captured using a 3D gait analysis system. Numerous gait characteristics can be derived from this gait data, and the early identification of AD can be facilitated by applying a graph neural network-based machine learning approach.