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<p>Alzheimer’s disease (AD), the leading cause of dementia, is a progressive neurodegenerative disorder that poses a major health challenge by significantly affecting life expectancy, quality of life, and overall well-being among the elderly. This form of progressive neurodegenerative disorder not only impacts healthcare systems but also imposes a significant psychological and economic burden on family and caregivers. Despite substantial progress, AD continues to challenge early and accurate diagnosis due to its complex etiology, variable progression, and overlapping clinical manifestations. Thus, a review has been conducted using SCOPUS database and external sources encompassing articles (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(N=1896\)</EquationSource> </InlineEquation>) of which articles (<InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(N=110\)</EquationSource> </InlineEquation>) met the selection criteria for comprehensive analysis. The objective of this study has been to evaluate existing research on the etiology, progression, and impact of AD with emphasis on how integrating biomarkers and assessment methods advances automated diagnostic classification. Thus, this review critically examined assessment methods, explored multimodal implications, and highlighted the identification of peripheral biomarkers relevant to automated diagnosis and disease monitoring. The findings suggest that recent advancements have been noted across the use of multimodal approaches and peripheral biomarker integration in AD research. Additionally, the reviewed studies indicate that automated methods addressing the challenges of multimodal datasets and classification models provide robust frameworks for developing accurate and scalable diagnostic systems for AD. Overall, these advancements underscore the potential of automated multimodal frameworks through synergistic fusion of biomarkers, assessment methods and computational models to improve the accuracy, robustness and scalability in early classification of AD.</p>

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Automated Advancements and Synergistic Challenges in Multi-Modal Clinical Assessments for Alzheimer’s Disease Diagnosis: A Comprehensive Review and Meta-Analysis

  • 期刊论文

  • 发表日期：

    2025年11月13日

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  • Manisha Rajput,
  • Puneet Bawa,
  • Virender Kadyan,
  • Monika Sethi

摘要

Alzheimer’s disease (AD), the leading cause of dementia, is a progressive neurodegenerative disorder that poses a major health challenge by significantly affecting life expectancy, quality of life, and overall well-being among the elderly. This form of progressive neurodegenerative disorder not only impacts healthcare systems but also imposes a significant psychological and economic burden on family and caregivers. Despite substantial progress, AD continues to challenge early and accurate diagnosis due to its complex etiology, variable progression, and overlapping clinical manifestations. Thus, a review has been conducted using SCOPUS database and external sources encompassing articles ( \(N=1896\) ) of which articles ( \(N=110\) ) met the selection criteria for comprehensive analysis. The objective of this study has been to evaluate existing research on the etiology, progression, and impact of AD with emphasis on how integrating biomarkers and assessment methods advances automated diagnostic classification. Thus, this review critically examined assessment methods, explored multimodal implications, and highlighted the identification of peripheral biomarkers relevant to automated diagnosis and disease monitoring. The findings suggest that recent advancements have been noted across the use of multimodal approaches and peripheral biomarker integration in AD research. Additionally, the reviewed studies indicate that automated methods addressing the challenges of multimodal datasets and classification models provide robust frameworks for developing accurate and scalable diagnostic systems for AD. Overall, these advancements underscore the potential of automated multimodal frameworks through synergistic fusion of biomarkers, assessment methods and computational models to improve the accuracy, robustness and scalability in early classification of AD.