[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116899-en":3,"doc-seo-116899-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},116899,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Machine Learning for Alzheimer’s Disease and Related Dementias - Chapter 25","Dementia is a progressive set of cognitive and behavioral impairments driven by brain damage, commonly caused by Alzheimer’s disease, vascular dementia, or frontotemporal dementia. Pathological processes often begin a decade before clinical symptoms, motivating the use of biomarkers from genetics, biofluids, medical images, and clinical/cognitive assessments. Machine learning approaches—including data harmonization, biomarker extraction, classification, and disease-progression modeling—have expanded in the literature, yet few methods are translated into routine clinical care, leaving major challenges to address.","Chapter 25  \nMachine Learning for Alzheimer’s Disease and Related Dementias  \nMarc Modat, David M. Cash, Liane Dos Santos Canas, Martina Bocchetta, and S´ebastien Ourselin  \nAbstract  \nDementia denotes the condition that affects people suffering from cognitive and behavioral impairments due to brain damage. Common causes of dementia include Alzheimer’s disease, vascular dementia, or frontotemporal dementia, among others. The onset of these pathologies often occurs at least a decade before any clinical symptoms are perceived. Several biomarkers have been developed to gain a better insight into disease progression, both in the prodromal and the symptomatic phases. Those markers are commonly derived from genetic information, biofluid, medical images, or clinical and cognitive assessments. Information is nowadays also captured using smart devices to further understand how patients are affected. In the last two to three decades, the research community has made a great effort to capture and share for research a large amount of data from many sources. As a result, many approaches using machine learning have been proposed in the scientific literature. Those include dedicated tools for data harmonization, extraction of biomarkers that act as disease progression proxy, classification tools, or creation of focused modeling tools that mimic and help predict disease progression. To date, however, very few methods have been translated to clinical care, and many challenges still need addressing.  \nKey words Dementia, Alzheimer’s disease, Cognitive impairment, Machine learning, Data harmonization, Biomarkers, Imaging, Classification, Disease progression modeling  \n1 Introduction  \nDementia is a progressive condition which affects over 55 million people worldwide, with nearly 10 million new cases every year  \n[1] . The term “dementia” indicates not a single disease, but rather a spectrum of different conditions with different clinical phenotypes, which can be caused by a multitude of pathologies that cause changes in the structure and chemistry of the brain. While the most common cause of dementia-related symptoms is a neurodegenerative disease, other causes do exist (e.g., chronic inflammatory disease, alcoholism... ). The exact pathological cascade of events which causes the development of symptoms is still unknown, but overall it  \nOlivier Colliot (ed.), Machine Learning for Brain Disorders, Neuromethods, vol. 197, [https://doi.org/10.1007/978-1-0716-3195-9_25](https://doi.org/10.1007/978-1-0716-3195-9_25) ,© The Author(s) 2023  \n808 Marc Modat et al.  \n1.1 Alzheimer’s Disease (AD)  \nis thought that a combination of genetic and environmental factors results in the abnormal accumulation of misfolded, toxic proteins in the brain, which then triggers both chemical imbalance and neuronal loss in the brain (a process called atrophy), ultimately leading to the hallmark clinical symptoms that eventually impair the daily functioning of affected individuals. An important distinction to make is between the concept of “dementia” as a collection of clinical syndromes and as qualitative and quantitative clinical expressions of the disease, and “disease” as the underlying pathophysiological processes of the syndromes.  \nThanks to the increased insight into disease pathophysiology, there has been a revision of the clinical diagnostic criteria, moving from considering the observable clinical signs and symptoms and implying a close and consistent correspondence between clinical symptoms and the underlying pathology, to including biomarkers of the underlying disease state in the clinical diagnosis. For example, the 1984 NINCDS-ADRDA1 criteria were the benchmark fora clinical diagnosis of Alzheimer’s disease, which was defined as “a progressive, dementing disorder, usually of middle or late life”  \n[2] . These criteria were revised in 2011 [3], to include biomarkers to support the clinical diagnosis and to account for the “predementia” stages and the slow pathologi","cbCaikvn8UEaVHHJ","https://ap.wps.com/l/cbCaikvn8UEaVHHJ","pdf",986265,1,40,"English","en",105,"# Introduction\n## Alzheimer’s Disease (AD)","[{\"question\":\"What are the main causes and characteristics of dementia discussed in the chapter?\",\"answer\":\"Dementia results from brain damage causing cognitive and behavioral impairments, with common causes including Alzheimer’s disease, vascular dementia, and frontotemporal dementia. It is progressive and reflects a spectrum of conditions with different clinical phenotypes.\"},{\"question\":\"Why are biomarkers important for Alzheimer’s disease and related dementias?\",\"answer\":\"Biomarkers help understand disease progression because pathological changes and prodromal stages can occur at least a decade before clinical symptoms. They support more accurate clinical diagnosis by reflecting the underlying disease state.\"},{\"question\":\"Which machine learning approaches are highlighted for ADRD research and what limits remain for clinical translation?\",\"answer\":\"The chapter highlights tools for data harmonization, biomarker extraction for disease-progression proxy, classification tools, and modeling tools aimed at predicting progression. Despite many proposed methods, few have been translated into clinical care, and challenges remain.\"}]","Machine Learning for Alzheimer’s Disease and Related Dementias - Chapter 25 | PDF",1785672340,101,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"machine-learning-for-alzheimers-disease-and-related-dementias-chapter-25","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-alzheimers-disease-and-related-dementias-chapter-25/116899/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What are the main causes and characteristics of dementia discussed in the chapter?","Question",{"text":75,"@type":76},"Dementia results from brain damage causing cognitive and behavioral impairments, with common causes including Alzheimer’s disease, vascular dementia, and frontotemporal dementia. It is progressive and reflects a spectrum of conditions with different clinical phenotypes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why are biomarkers important for Alzheimer’s disease and related dementias?",{"text":80,"@type":76},"Biomarkers help understand disease progression because pathological changes and prodromal stages can occur at least a decade before clinical symptoms. They support more accurate clinical diagnosis by reflecting the underlying disease state.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approaches are highlighted for ADRD research and what limits remain for clinical translation?",{"text":84,"@type":76},"The chapter highlights tools for data harmonization, biomarker extraction for disease-progression proxy, classification tools, and modeling tools aimed at predicting progression. 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