# Progress in Machine Learning-Assisted Biosensors for Alzheimer’s Disease

## TL;DR

This paper reviews how machine learning helps detect Alzheimer’s disease biomarkers in bodily fluids, aiming to improve early diagnosis.

## Contribution

The paper provides a comprehensive overview of recent advances in machine learning-assisted biosensors for Alzheimer’s disease detection.

## Key findings

- Machine learning algorithms are being integrated with biosensors to detect AD biomarkers like microRNAs and Tau protein.
- Electrochemical and optical biosensors assisted by machine learning show promise for early Alzheimer’s diagnosis.
- Challenges remain in translating these technologies into reliable clinical tools for neurodegenerative disease management.

## Abstract

Alzheimer’s disease (AD) is the most common cause of dementia, affecting 55 million people worldwide. Its characteristics include the accumulation of senile plaques and neurofibrillary tangles. This disease is associated with changes in the concentration of AD biomarkers, such as microRNAs, amyloid peptides, Tau protein, and neurofilament light chains. Due to the fact that neuropathological processes begin decades before the onset of cognitive symptoms, accurate detection of AD biomarkers is crucial for its early diagnosis. The combination of analytical techniques and machine learning methods plays a crucial role in medical innovation. Recently, efforts have been made to develop machine learning-assisted biosensors for AD diagnosis. This article provides an overview of the progress in machine learning-assisted sensing of AD biomarkers in bodily fluids. It mainly includes three parts: machine learning algorithms, machine learning-assisted electrochemical and optical biosensors, and challenges and future perspectives. We believe that this work will contribute to the development of innovative analytical devices based on artificial intelligence for monitoring and managing neurodegenerative diseases.