Advanced Techniques for the Early Detection of Alzheimer’s Disease Using Neuroimaging and Machine Learning
摘要
Alzheimer’s Disease (AD) is a catastrophic condition that progresses in the human brain with mild cognitive impairments leading to profound memory impairment and associated changes in behavior, all of which combine to degrade life quality. Background and significance AD is the most common cause of dementia across the world. Given its escalating prevalence alongside aging populations, it represents a considerable public health problem. Histopathology is characterized by beta-amyloid plaques and the tau neurofibrillary tangles in the brain with ensuing synaptic dysfunction, neuronal loss, and cognitive decline (Nelson et al. 2009). In the clinical setting, AD is now conceptualized as passing through a phase of mild cognitive impairment on its way to becoming severe dementia and targets multiple areas including memory, language capabilities, visuospatial abilities, and executive control. AD remains a clinical diagnosis, relying on history and physical examination, additional testing like neuropsychologic studies, or the use of neuroimaging MRI and PET. Biomarker field studies can provide findings for CSF markers and possibly blood-based biomarkers, advancing our knowledge about the disease’s underlying pathology and early detection. Current Treatment of ADAD treatment is quite limited to symptomatic management and disease-modifying approach targeting amyloid and tau pathology. This review discusses the clinical, pathology, and etiology of Alzheimer’s Disease and the diagnostic criteria for AD and associated imaging tools, including neuropsychological tests. Further combined research efforts are imperative to decode the multifaceted avenues of AD, create groundbreaking diagnostic techniques, and propel therapeutic interventions for a very serious neurological disorder worldwide.