Advancing Healthcare: Synergizing Biosensors and Machine Learning for Early Cancer Diagnosis.

Area of Science:

• Biomedical Engineering
• Computational Biology
• Oncology

Background:

• Cancer remains a leading cause of mortality, often diagnosed late due to limitations in traditional methods.
• Early detection is vital for effective treatment, necessitating accessible and rapid diagnostic tools.
• Point-of-care (POC) biosensors offer a promising alternative for timely cancer diagnosis.

Purpose of the Study:

• To review emerging technologies in POC cancer diagnostic biosensors.
• To explore the role of machine learning (ML) algorithms in processing biosensor data.
• To compare the performance of different ML algorithms and sensing modalities for cancer classification.

Main Methods:

• Review of recent advancements in POC biosensor technology and ML applications.
• Analysis of statistical techniques used by ML algorithms for biosensor data interpretation.
• Comparative assessment of ML algorithms and sensing platforms based on classification accuracy.

Main Results:

• Miniaturization and cost reduction have made POC biosensors more practical for cancer diagnostics.
• ML algorithms enhance data processing for biosensors, extracting valuable diagnostic information.
• Performance metrics, including classification accuracy, vary across different ML algorithms and sensing modalities.

Conclusions:

• POC biosensors integrated with ML show significant potential for early and accessible cancer diagnosis.
• Continued development in ML and biosensor technology is crucial for advancing cancer detection strategies.
• Optimizing ML algorithms and sensing platforms can lead to improved diagnostic accuracy and patient outcomes.