Where Nanosensors Meet Machine Learning: Prospects and Challenges in Detecting Disease X
Yong Xiang Leong, Emily Xi Tan, Shi Xuan Leong, Charlynn Sher Lin Koh, Lam Bang Thanh Nguyen, Jaslyn Ru Ting Chen, Kelin Xia, Xing Yi Ling
Abstract
Disease X is a hypothetical unknown disease that has the potential to cause an epidemic or pandemic outbreak in the future. Nanosensors are attractive portable devices that can swiftly screen disease biomarkers on site, reducing the reliance on laboratory-based analyses. However, conventional data analytics limit the progress of nanosensor research. In this Perspective, we highlight the integral role of machine learning (ML) algorithms in advancing nanosensing strategies toward Disease X detection. We first summarize recent progress in utilizing ML algorithms for the smart design and fabrication of custom nanosensor platforms as well as realizing rapid on-site prediction of infection statuses. Subsequently, we discuss promising prospects in further harnessing the potential of ML algorithms in other aspects of nanosensor development and biomarker detection.