From pixels to prognosis: radiomics and AI in Alzheimer’s disease management DOI Creative Commons
Dequn Peng, Weiyi Huang, Ren Ping Liu

и другие.

Frontiers in Neurology, Год журнала: 2025, Номер 16

Опубликована: Янв. 29, 2025

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 management, yet traditional diagnostic methods often fall short in addressing complexity AD pathology. Recent advancements radiomics artificial intelligence (AI) offer novel solutions by integrating quantitative imaging features machine learning algorithms enhance prognostic precision. This review explores application AI AD, focusing on key modalities such as PET MRI, well multimodal approaches combining structural functional data. We discuss potential these technologies identify disease-specific biomarkers, predict progression, guide personalized interventions. Additionally, addresses critical challenges, including data standardization, model interpretability, integration into clinical workflows. By highlighting current achievements identifying future directions, this article underscores transformative AI-driven reshaping diagnostics care.

Язык: Английский

From pixels to prognosis: radiomics and AI in Alzheimer’s disease management DOI Creative Commons
Dequn Peng, Weiyi Huang, Ren Ping Liu

и другие.

Frontiers in Neurology, Год журнала: 2025, Номер 16

Опубликована: Янв. 29, 2025

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 management, yet traditional diagnostic methods often fall short in addressing complexity AD pathology. Recent advancements radiomics artificial intelligence (AI) offer novel solutions by integrating quantitative imaging features machine learning algorithms enhance prognostic precision. This review explores application AI AD, focusing on key modalities such as PET MRI, well multimodal approaches combining structural functional data. We discuss potential these technologies identify disease-specific biomarkers, predict progression, guide personalized interventions. Additionally, addresses critical challenges, including data standardization, model interpretability, integration into clinical workflows. By highlighting current achievements identifying future directions, this article underscores transformative AI-driven reshaping diagnostics care.

Язык: Английский

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