Progress in Machine Learning-Assisted Biosensors for Alzheimer's Disease.

Area of Science:

• Neuroscience
• Biomedical Engineering
• Artificial Intelligence

Background:

• Alzheimer's disease (AD) is the leading cause of dementia, impacting millions globally.
• AD is characterized by amyloid plaques and neurofibrillary tangles, with biomarker concentration changes preceding cognitive symptoms.
• Early diagnosis is critical due to the long preclinical phase of neuropathological processes.

Purpose of the Study:

• To provide an overview of machine learning-assisted sensing of Alzheimer's disease biomarkers.
• To explore the integration of analytical techniques and machine learning for AD diagnosis.
• To discuss challenges and future directions in AI-driven neurodegenerative disease monitoring.

Main Methods:

• Review of machine learning algorithms applied to AD biomarker detection.
• Analysis of machine learning-assisted electrochemical and optical biosensors for sensing AD biomarkers.
• Examination of current progress and future perspectives in the field.

Main Results:

• Machine learning combined with biosensors offers a promising approach for early AD detection.
• Various analytical techniques are being enhanced by machine learning for biomarker quantification.
• Significant progress has been made in developing AI-assisted devices for neurodegenerative disease management.

Conclusions:

• Machine learning-assisted biosensors are crucial for the early diagnosis of Alzheimer's disease.
• The integration of AI and biosensing holds potential for innovative analytical devices.
• Continued research is needed to overcome challenges and advance AI-driven neurodegenerative disease monitoring.