Deep Learning Enabled Universal Multiplexed Fluorescence Detection for Point-of-Care Applications.

ABSTRACT: There is a significant demand for multiplexed fluorescence sensing and detection across a range of applications. Yet, the development of portable and compact multiplexable systems remains a substantial challenge. This difficulty largely stems from the inherent need for spectrum separation, which typically requires sophisticated and expensive optical components. Here, we demonstrate a compact, lens-free, and cost-effective fluorescence sensing setup that incorporates machine learning for scalable multiplexed fluorescence detection. This method utilizes low-cost optical components and a pretrained machine learning (ML) model to enable multiplexed fluorescence sensing without optical adjustments. Its multiplexing capability can be easily scaled up through updates to the machine learning model

SUBMITTER: Kshirsagar A 

PROVIDER: S-EPMC11421847 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature