From pixels to prognosis: radiomics and AI in Alzheimer’s disease management
Alzheimer’s disease (AD), the leading cause of dementia, poses a growing global health challenge due to an aging population. Early and accurate diagnosis is essential for optimizing treatment and management, yet traditional diagnostic methods often fall short in addressing the complexity of AD patho...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Neurology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fneur.2025.1536463/full |
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| _version_ | 1832582506656825344 |
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| author | Danting Peng Weiju Huang Ren Liu Wenlong Zhong |
| author_facet | Danting Peng Weiju Huang Ren Liu Wenlong Zhong |
| author_sort | Danting Peng |
| collection | DOAJ |
| description | Alzheimer’s disease (AD), the leading cause of dementia, poses a growing global health challenge due to an aging population. Early and accurate diagnosis is essential for optimizing treatment and management, yet traditional diagnostic methods often fall short in addressing the complexity of AD pathology. Recent advancements in radiomics and artificial intelligence (AI) offer novel solutions by integrating quantitative imaging features and machine learning algorithms to enhance diagnostic and prognostic precision. This review explores the application of radiomics and AI in AD, focusing on key imaging modalities such as PET and MRI, as well as multimodal approaches combining structural and functional data. We discuss the potential of these technologies to identify disease-specific biomarkers, predict disease progression, and guide personalized interventions. Additionally, the review addresses critical challenges, including data standardization, model interpretability, and the integration of AI into clinical workflows. By highlighting current achievements and identifying future directions, this article underscores the transformative potential of AI-driven radiomics in reshaping AD diagnostics and care. |
| format | Article |
| id | doaj-art-41490da2027b47d9b16608755d4d4d6e |
| institution | Kabale University |
| issn | 1664-2295 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurology |
| spelling | doaj-art-41490da2027b47d9b16608755d4d4d6e2025-01-29T14:52:00ZengFrontiers Media S.A.Frontiers in Neurology1664-22952025-01-011610.3389/fneur.2025.15364631536463From pixels to prognosis: radiomics and AI in Alzheimer’s disease managementDanting PengWeiju HuangRen LiuWenlong ZhongAlzheimer’s disease (AD), the leading cause of dementia, poses a growing global health challenge due to an aging population. Early and accurate diagnosis is essential for optimizing treatment and management, yet traditional diagnostic methods often fall short in addressing the complexity of AD pathology. Recent advancements in radiomics and artificial intelligence (AI) offer novel solutions by integrating quantitative imaging features and machine learning algorithms to enhance diagnostic and prognostic precision. This review explores the application of radiomics and AI in AD, focusing on key imaging modalities such as PET and MRI, as well as multimodal approaches combining structural and functional data. We discuss the potential of these technologies to identify disease-specific biomarkers, predict disease progression, and guide personalized interventions. Additionally, the review addresses critical challenges, including data standardization, model interpretability, and the integration of AI into clinical workflows. By highlighting current achievements and identifying future directions, this article underscores the transformative potential of AI-driven radiomics in reshaping AD diagnostics and care.https://www.frontiersin.org/articles/10.3389/fneur.2025.1536463/fullAlzheimer’s diseaseADradiomicsartificial intelligencedeep learningneurodegenerative diseases |
| spellingShingle | Danting Peng Weiju Huang Ren Liu Wenlong Zhong From pixels to prognosis: radiomics and AI in Alzheimer’s disease management Frontiers in Neurology Alzheimer’s disease AD radiomics artificial intelligence deep learning neurodegenerative diseases |
| title | From pixels to prognosis: radiomics and AI in Alzheimer’s disease management |
| title_full | From pixels to prognosis: radiomics and AI in Alzheimer’s disease management |
| title_fullStr | From pixels to prognosis: radiomics and AI in Alzheimer’s disease management |
| title_full_unstemmed | From pixels to prognosis: radiomics and AI in Alzheimer’s disease management |
| title_short | From pixels to prognosis: radiomics and AI in Alzheimer’s disease management |
| title_sort | from pixels to prognosis radiomics and ai in alzheimer s disease management |
| topic | Alzheimer’s disease AD radiomics artificial intelligence deep learning neurodegenerative diseases |
| url | https://www.frontiersin.org/articles/10.3389/fneur.2025.1536463/full |
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