Two-dimensional nanomaterials-based optical biosensors empowered by machine learning for intelligent diagnosis

Abstract

Artificial intelligence techniques based on machine learning (ML) are prevalent in healthcare for disease diagnosis, drug discovery, and patient risk identification. Two-dimensional nanomaterials (2DMs)-based optical biosensors have received extensive attention because of their high sensitivity, easy operation and fast response. Recent advances have been achieved for 2DMs-based optical biosensors empowered by ML algorithms for intelligent diagnosis. In this review, we present a comprehensive summary about ML algorithms-assisted 2DMs-based optical biosensors for intelligent diagnosis. We first introduce basic principles of ML algorithms and describe the advantages of 2DMs as sensing materials in optical biosensors. Then, we summarize the recent development of ML-assisted 2DMs-based optical biosensors, particularly focusing on fluorescence biosensors, colorimetric biosensors, and Raman biosensors. Rational selections of ML algorithms are recommended based on the forms of the biosensor data and target analytes. It ends by listing the current challenges and proposing future trends of ML-assisted 2DMs-based optical biosensors for intelligent diagnosis.