Clinical Decision Support for Alzheimer’s: Challenges in Generalizable Data-Driven Approach
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IOS Press
Abstract
This paper reviews the current research on Alzheimer's disease and the use of deep learning, particularly 3D-convolutional neural networks (3D-CNN), in analyzing brain images. It presents a predictive model based on MRI and clinical data from the ADNI dataset, showing that deep learning can improve diagnosis accuracy and sensitivity. We also discuss potential applications in biomarker discovery, disease progression prediction, and personalised treatment planning, highlighting the ability to identify sensitive features for early diagnosis.Description
Keywords
3D-CNN, Deep Learning, Digital health, Neurodegenerative Diseases, 3D-CNN, Deep Learning, Digital health, Neurodegenerative Diseases, 46 Information and Computing Sciences, 4611 Machine Learning, Alzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD), Biomedical Imaging, Aging, Brain Disorders, Neurosciences, Bioengineering, Alzheimer's Disease, Dementia, Networking and Information Technology R&D (NITRD), Acquired Cognitive Impairment, Prevention, Neurodegenerative, Machine Learning and Artificial Intelligence, 4.1 Discovery and preclinical testing of markers and technologies, 4.2 Evaluation of markers and technologies, Neurological, 3 Good Health and Well Being, 0807 Library and Information Studies, 1117 Public Health and Health Services, Medical Informatics, 4203 Health services and systems, 4601 Applied computing
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Studies in Health Technology and Informatics, ISSN: 0926-9630 (Print); 0926-9630 (Online), IOS Press, 329, 1780-1781. doi: 10.3233/SHTI251211
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© 2025 The Authors. This article is published online with Open Access by IOS Press and distributed under the terms of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).