Shuichi P Obuchi, Motonaga Kojima, Hiroyuki Suzuki, Juan C Garbalosa, Keigo Imamura, Kazushige Ihara, Hirohiko Hirano, Hiroyuki Sasai, Yoshinori Fujiwara, Hisashi Kawai
{"title":"人工智能检测老年人行走时的认知障碍。","authors":"Shuichi P Obuchi, Motonaga Kojima, Hiroyuki Suzuki, Juan C Garbalosa, Keigo Imamura, Kazushige Ihara, Hirohiko Hirano, Hiroyuki Sasai, Yoshinori Fujiwara, Hisashi Kawai","doi":"10.1002/dad2.70012","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>To detect early cognitive impairment in community-dwelling older adults, this study explored the viability of artificial intelligence (AI)-assisted linear acceleration and angular velocity analysis during walking.</p><p><strong>Methods: </strong>This cross-sectional study included 879 participants without dementia (female, 60.6%; mean age, 73.5 years) from the 2011 Comprehensive Gerontology Survey. Sensors attached to the pelvis and left ankle recorded the triaxial linear acceleration and angular velocity while the participants walked at a comfortable speed. Cognitive impairment was determined using Mini-Mental State Examination scores. Deep learning models were used to discern the linear acceleration and angular velocity data of 12,302 walking strides.</p><p><strong>Results: </strong>The models' average sensitivity, specificity, and area under the curve were 0.961, 0.643, and 0.833, respectively, across 30 testing datasets.</p><p><strong>Discussion: </strong>AI-enabled gait analysis can be used to detect signs of cognitive impairment. Integrating this AI model into smartphones may help detect dementia early, facilitating better prevention.</p><p><strong>Highlights: </strong>Artificial intelligence (AI)-enabled gait analysis can be used to detect the early signs of cognitive decline.This AI model was constructed using data from a community-dwelling cohort.AI-assisted linear acceleration and angular velocity analysis during gait was used.The model may help in early detection of dementia.</p>","PeriodicalId":53226,"journal":{"name":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","volume":"16 3","pages":"e70012"},"PeriodicalIF":4.0000,"publicationDate":"2024-09-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424983/pdf/","citationCount":"0","resultStr":"{\"title\":\"Artificial intelligence detection of cognitive impairment in older adults during walking.\",\"authors\":\"Shuichi P Obuchi, Motonaga Kojima, Hiroyuki Suzuki, Juan C Garbalosa, Keigo Imamura, Kazushige Ihara, Hirohiko Hirano, Hiroyuki Sasai, Yoshinori Fujiwara, Hisashi Kawai\",\"doi\":\"10.1002/dad2.70012\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>To detect early cognitive impairment in community-dwelling older adults, this study explored the viability of artificial intelligence (AI)-assisted linear acceleration and angular velocity analysis during walking.</p><p><strong>Methods: </strong>This cross-sectional study included 879 participants without dementia (female, 60.6%; mean age, 73.5 years) from the 2011 Comprehensive Gerontology Survey. Sensors attached to the pelvis and left ankle recorded the triaxial linear acceleration and angular velocity while the participants walked at a comfortable speed. Cognitive impairment was determined using Mini-Mental State Examination scores. Deep learning models were used to discern the linear acceleration and angular velocity data of 12,302 walking strides.</p><p><strong>Results: </strong>The models' average sensitivity, specificity, and area under the curve were 0.961, 0.643, and 0.833, respectively, across 30 testing datasets.</p><p><strong>Discussion: </strong>AI-enabled gait analysis can be used to detect signs of cognitive impairment. Integrating this AI model into smartphones may help detect dementia early, facilitating better prevention.</p><p><strong>Highlights: </strong>Artificial intelligence (AI)-enabled gait analysis can be used to detect the early signs of cognitive decline.This AI model was constructed using data from a community-dwelling cohort.AI-assisted linear acceleration and angular velocity analysis during gait was used.The model may help in early detection of dementia.</p>\",\"PeriodicalId\":53226,\"journal\":{\"name\":\"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring\",\"volume\":\"16 3\",\"pages\":\"e70012\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-09-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11424983/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/dad2.70012\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/7/1 0:00:00\",\"PubModel\":\"eCollection\",\"JCR\":\"Q1\",\"JCRName\":\"CLINICAL NEUROLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alzheimer''s and Dementia: Diagnosis, Assessment and Disease Monitoring","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/dad2.70012","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/1 0:00:00","PubModel":"eCollection","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
Artificial intelligence detection of cognitive impairment in older adults during walking.
Introduction: To detect early cognitive impairment in community-dwelling older adults, this study explored the viability of artificial intelligence (AI)-assisted linear acceleration and angular velocity analysis during walking.
Methods: This cross-sectional study included 879 participants without dementia (female, 60.6%; mean age, 73.5 years) from the 2011 Comprehensive Gerontology Survey. Sensors attached to the pelvis and left ankle recorded the triaxial linear acceleration and angular velocity while the participants walked at a comfortable speed. Cognitive impairment was determined using Mini-Mental State Examination scores. Deep learning models were used to discern the linear acceleration and angular velocity data of 12,302 walking strides.
Results: The models' average sensitivity, specificity, and area under the curve were 0.961, 0.643, and 0.833, respectively, across 30 testing datasets.
Discussion: AI-enabled gait analysis can be used to detect signs of cognitive impairment. Integrating this AI model into smartphones may help detect dementia early, facilitating better prevention.
Highlights: Artificial intelligence (AI)-enabled gait analysis can be used to detect the early signs of cognitive decline.This AI model was constructed using data from a community-dwelling cohort.AI-assisted linear acceleration and angular velocity analysis during gait was used.The model may help in early detection of dementia.
期刊介绍：
Alzheimer''s & Dementia: Diagnosis, Assessment & Disease Monitoring (DADM) is an open access, peer-reviewed, journal from the Alzheimer''s Association® that will publish new research that reports the discovery, development and validation of instruments, technologies, algorithms, and innovative processes. Papers will cover a range of topics interested in the early and accurate detection of individuals with memory complaints and/or among asymptomatic individuals at elevated risk for various forms of memory disorders. The expectation for published papers will be to translate fundamental knowledge about the neurobiology of the disease into practical reports that describe both the conceptual and methodological aspects of the submitted scientific inquiry. Published topics will explore the development of biomarkers, surrogate markers, and conceptual/methodological challenges. Publication priority will be given to papers that 1) describe putative surrogate markers that accurately track disease progression, 2) biomarkers that fulfill international regulatory requirements, 3) reports from large, well-characterized population-based cohorts that comprise the heterogeneity and diversity of asymptomatic individuals and 4) algorithmic development that considers multi-marker arrays (e.g., integrated-omics, genetics, biofluids, imaging, etc.) and advanced computational analytics and technologies.
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