{"title":"Revolutionizing dementia detection: Leveraging vision and Swin transformers for early diagnosis","authors":"Rini P L, Gayathri K S","doi":"10.1002/ajmg.b.32979","DOIUrl":null,"url":null,"abstract":"<p>Dementia, an increasingly prevalent neurological disorder with a projected threefold rise globally by 2050, necessitates early detection for effective management. The risk notably increases after age 65. Dementia leads to a progressive decline in cognitive functions, affecting memory, reasoning, and problem-solving abilities. This decline can impact the individual's ability to perform daily tasks and make decisions, underscoring the crucial importance of timely identification. With the advent of technologies like computer vision and deep learning, the prospect of early detection becomes even more promising. Employing sophisticated algorithms on imaging data, such as positron emission tomography scans, facilitates the recognition of subtle structural brain changes, enabling diagnosis at an earlier stage for potentially more effective interventions. In an experimental study, the Swin transformer algorithm demonstrated superior overall accuracy compared to the vision transformer and convolutional neural network, emphasizing its efficiency. Detecting dementia early is essential for proactive management, personalized care, and implementing preventive measures, ultimately enhancing outcomes for individuals and lessening the overall burden on healthcare systems.</p>","PeriodicalId":7673,"journal":{"name":"American Journal of Medical Genetics Part B: Neuropsychiatric Genetics","volume":"195 7","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Medical Genetics Part B: Neuropsychiatric Genetics","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/ajmg.b.32979","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"GENETICS & HEREDITY","Score":null,"Total":0}
引用次数: 0
Abstract
Dementia, an increasingly prevalent neurological disorder with a projected threefold rise globally by 2050, necessitates early detection for effective management. The risk notably increases after age 65. Dementia leads to a progressive decline in cognitive functions, affecting memory, reasoning, and problem-solving abilities. This decline can impact the individual's ability to perform daily tasks and make decisions, underscoring the crucial importance of timely identification. With the advent of technologies like computer vision and deep learning, the prospect of early detection becomes even more promising. Employing sophisticated algorithms on imaging data, such as positron emission tomography scans, facilitates the recognition of subtle structural brain changes, enabling diagnosis at an earlier stage for potentially more effective interventions. In an experimental study, the Swin transformer algorithm demonstrated superior overall accuracy compared to the vision transformer and convolutional neural network, emphasizing its efficiency. Detecting dementia early is essential for proactive management, personalized care, and implementing preventive measures, ultimately enhancing outcomes for individuals and lessening the overall burden on healthcare systems.
期刊介绍:
Neuropsychiatric Genetics, Part B of the American Journal of Medical Genetics (AJMG) , provides a forum for experimental and clinical investigations of the genetic mechanisms underlying neurologic and psychiatric disorders. It is a resource for novel genetics studies of the heritable nature of psychiatric and other nervous system disorders, characterized at the molecular, cellular or behavior levels. Neuropsychiatric Genetics publishes eight times per year.