Yihan Wang, Shu Liu, Alanna G Spiteri, Andrew Liem Hieu Huynh, Chenyin Chu, Colin L Masters, Benjamin Goudey, Yijun Pan, Liang Jin
{"title":"Understanding machine learning applications in dementia research and clinical practice: a review for biomedical scientists and clinicians.","authors":"Yihan Wang, Shu Liu, Alanna G Spiteri, Andrew Liem Hieu Huynh, Chenyin Chu, Colin L Masters, Benjamin Goudey, Yijun Pan, Liang Jin","doi":"10.1186/s13195-024-01540-6","DOIUrl":null,"url":null,"abstract":"<p><p>Several (inter)national longitudinal dementia observational datasets encompassing demographic information, neuroimaging, biomarkers, neuropsychological evaluations, and muti-omics data, have ushered in a new era of potential for integrating machine learning (ML) into dementia research and clinical practice. ML, with its proficiency in handling multi-modal and high-dimensional data, has emerged as an innovative technique to facilitate early diagnosis, differential diagnosis, and to predict onset and progression of mild cognitive impairment and dementia. In this review, we evaluate current and potential applications of ML, including its history in dementia research, how it compares to traditional statistics, the types of datasets it uses and the general workflow. Moreover, we identify the technical barriers and challenges of ML implementations in clinical practice. Overall, this review provides a comprehensive understanding of ML with non-technical explanations for broader accessibility to biomedical scientists and clinicians.</p>","PeriodicalId":7516,"journal":{"name":"Alzheimer's Research & Therapy","volume":"16 1","pages":"175"},"PeriodicalIF":7.9000,"publicationDate":"2024-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11293066/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Alzheimer's Research & Therapy","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13195-024-01540-6","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Several (inter)national longitudinal dementia observational datasets encompassing demographic information, neuroimaging, biomarkers, neuropsychological evaluations, and muti-omics data, have ushered in a new era of potential for integrating machine learning (ML) into dementia research and clinical practice. ML, with its proficiency in handling multi-modal and high-dimensional data, has emerged as an innovative technique to facilitate early diagnosis, differential diagnosis, and to predict onset and progression of mild cognitive impairment and dementia. In this review, we evaluate current and potential applications of ML, including its history in dementia research, how it compares to traditional statistics, the types of datasets it uses and the general workflow. Moreover, we identify the technical barriers and challenges of ML implementations in clinical practice. Overall, this review provides a comprehensive understanding of ML with non-technical explanations for broader accessibility to biomedical scientists and clinicians.
一些(国家间)纵向痴呆症观察数据集涵盖了人口统计学信息、神经影像学、生物标记物、神经心理学评估和突变组学数据,为将机器学习(ML)融入痴呆症研究和临床实践开创了一个新时代。机器学习能熟练处理多模态和高维数据,已成为促进早期诊断、鉴别诊断以及预测轻度认知障碍和痴呆症发病和进展的创新技术。在这篇综述中,我们将评估 ML 的当前和潜在应用,包括其在痴呆症研究中的历史、与传统统计学的比较、使用的数据集类型以及一般工作流程。此外,我们还指出了在临床实践中实施 ML 的技术障碍和挑战。总之,这篇综述提供了对 ML 的全面了解,并提供了非技术性的解释,使生物医学科学家和临床医生更容易理解。
期刊介绍:
Alzheimer's Research & Therapy is an international peer-reviewed journal that focuses on translational research into Alzheimer's disease and other neurodegenerative diseases. It publishes open-access basic research, clinical trials, drug discovery and development studies, and epidemiologic studies. The journal also includes reviews, viewpoints, commentaries, debates, and reports. All articles published in Alzheimer's Research & Therapy are included in several reputable databases such as CAS, Current contents, DOAJ, Embase, Journal Citation Reports/Science Edition, MEDLINE, PubMed, PubMed Central, Science Citation Index Expanded (Web of Science) and Scopus.