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<p>Alzheimer’s Disease (AD), the most prevalent type of dementia, remains incurable, highlighting the critical need for early detection to slow its progression. AD often begins with years of asymptomatic development, making timely diagnosis essential. Advances in medical imaging and computational tools have introduced promising new avenues for diagnosing and understanding AD. This review focuses on the early detection of AD using deep learning, machine learning, and statistical methods. We analyze a range of approaches, including cognitive assessments, genetic data interpretation, and neuroimaging analysis, evaluating each method’s strengths and limitations. Comparative analysis highlights the diagnostic performance of these approaches and suggests pathways for their integration into clinical practice. Our findings support that combining these advanced techniques could significantly enhance the accuracy and reliability of early AD diagnosis, creating exciting possibilities for future research and clinical application.</p>

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A review on early detection of Alzheimer’s disease: employing deep learning, machine learning, and statistical methods

  • 期刊论文

  • 发表日期：

    2025年5月23日

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  • Soumyajit Biswas,
  • Anuvab Sen,
  • Subhabrata Roy

摘要

Alzheimer’s Disease (AD), the most prevalent type of dementia, remains incurable, highlighting the critical need for early detection to slow its progression. AD often begins with years of asymptomatic development, making timely diagnosis essential. Advances in medical imaging and computational tools have introduced promising new avenues for diagnosing and understanding AD. This review focuses on the early detection of AD using deep learning, machine learning, and statistical methods. We analyze a range of approaches, including cognitive assessments, genetic data interpretation, and neuroimaging analysis, evaluating each method’s strengths and limitations. Comparative analysis highlights the diagnostic performance of these approaches and suggests pathways for their integration into clinical practice. Our findings support that combining these advanced techniques could significantly enhance the accuracy and reliability of early AD diagnosis, creating exciting possibilities for future research and clinical application.