دورية أكاديمية
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Artificial intelligence for dementia research methods optimization.

التفاصيل البيبلوغرافية
العنوان: Artificial intelligence for dementia research methods optimization.
المؤلفون: Bucholc M; Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK., James C; NIHR Bristol Biomedical Research Centre, University Hospitals Bristol and Weston NHS Foundation Trust and University of Bristol, Bristol, UK., Khleifat AA; Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK., Badhwar A; Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada.; Institut de génie biomédical, Université de Montréal, Montréal, Quebec, Canada.; Département de Pharmacologie et Physiologie, Université de Montréal, Montréal, Quebec, Canada., Clarke N; Multiomics Investigation of Neurodegenerative Diseases (MIND) Lab, Centre de Recherche de l'Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada., Dehsarvi A; Aberdeen Biomedical Imaging Centre, School of Medicine, Medical Sciences, and Nutrition, University of Aberdeen, Aberdeen, UK., Madan CR; School of Psychology, University of Nottingham, Nottingham, UK., Marzi SJ; UK Dementia Research Institute, Imperial College London, London, UK.; Department of Brain Sciences, Imperial College London, London, UK., Shand C; Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK., Schilder BM; UK Dementia Research Institute, Imperial College London, London, UK.; Department of Brain Sciences, Imperial College London, London, UK., Tamburin S; Department of Neurosciences, Biomedicine and Movement Sciences, University of Verona, Verona, Italy., Tantiangco HM; Information School, University of Sheffield, Sheffield, UK., Lourida I; University of Exeter Medical School, Exeter, UK., Llewellyn DJ; University of Exeter Medical School, Exeter, UK.; The Alan Turing Institute, London, UK., Ranson JM; University of Exeter Medical School, Exeter, UK.
مؤلفون مشاركون: Deep Dementia Phenotyping (DEMON) Network; Cognitive Analytics Research Lab, School of Computing, Engineering & Intelligent Systems, Ulster University, Derry, UK.
المصدر: Alzheimer's & dementia : the journal of the Alzheimer's Association [Alzheimers Dement] 2023 Dec; Vol. 19 (12), pp. 5934-5951. Date of Electronic Publication: 2023 Aug 28.
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: John Wiley & Sons, Ltd Country of Publication: United States NLM ID: 101231978 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1552-5279 (Electronic) Linking ISSN: 15525260 NLM ISO Abbreviation: Alzheimers Dement Subsets: MEDLINE
أسماء مطبوعة: Publication: 2020- : Hoboken, NJ : John Wiley & Sons, Ltd.
Original Publication: Orlando, FL : Elsevier, Inc.
مواضيع طبية MeSH: Artificial Intelligence* , Dementia*/diagnosis, Humans ; Reproducibility of Results ; Machine Learning ; Research Design
مستخلص: Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.
(© 2023 The Authors. Alzheimer's & Dementia published by Wiley Periodicals LLC on behalf of Alzheimer's Association.)
التعليقات: Update of: ArXiv. 2023 Mar 2:arXiv:2303.01949v1.. (PMID: 36911275)
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معلومات مُعتمدة: RF1 AG055654 United States AG NIA NIH HHS; MR/X005674/1 United Kingdom MRC_ Medical Research Council
فهرسة مساهمة: Keywords: artificial intelligence; classification; clinical utility; deep learning; dementia; generalizability; interpretability; machine learning; methods optimization; regression; replicability; semi-supervised learning; supervised learning; transferability; unsupervised learning
تواريخ الأحداث: Date Created: 20230828 Date Completed: 20231229 Latest Revision: 20240712
رمز التحديث: 20240713
DOI: 10.1002/alz.13441
PMID: 37639369
قاعدة البيانات: MEDLINE
الوصف
تدمد:1552-5279
DOI:10.1002/alz.13441