Artificial Intelligence-Assisted Visualized Microspheres for Biochemical Analysis: From Encoding to Decoding

As an essential branch of chemical science, biochemical analysis is widely applied in disease diagnosis, food safety testing, environmental monitoring, and other fields. Artificial intelligence (AI) technology has substantially advanced biochemical analysis, enabling the prediction and extraction of key information from large volumes of image data; these tasks were previously unattainable, particularly in visualized single-microsphere counting for biosensing assays. By modification of different biorecognition molecules, encoded microspheres of various colors and sizes can serve as ideal optical multiprobes. In uniquely designed biochemical sensing systems, different encoded microspheres specifically associate with targets through signal recognition, resulting in corresponding changes in quantity as the target concentration varies after signal transduction. Images of these encoded microspheres are captured by optical imaging equipment and processed with high speed and precision by AI technology, which decodes them into the corresponding target concentration information. This approach, with its affordable cost and user-friendly operation, can be readily adopted for the rapid and sensitive multiplexed detection of various targets, including proteins, bacteria, viruses, and antibiotics.This Account summarizes recent studies conducted by our group on developing AI-assisted visualized microspheres for biosensing analysis and highlights unique encoding-decoding strategies and various biochemical approaches. We begin by introducing our encoding rules for different microsphere characteristics (e.g., color and size) and the preparation of encoded fluorescent microspheres, which can be conjugated with various biorecognition molecules to enable specific target capture or association. Subsequently, we summarize a series of biosensing platforms developed based on the MP encoding-AI decoding strategy; these platforms integrate different biochemical sensing approaches to meet diverse detection requirements and achieve efficient signal transduction tasks, such as immunoassays, click chemistry, Argonaute (Ago) systems, clustered regularly interspaced short palindromic repeats (CRISPR) systems, and microfluidics. Meanwhile, we present customized high-speed decoding algorithms based on AI technologies such as computer vision, machine learning, deep learning, and unsupervised learning to enable the accurate processing of encoded microsphere images acquired by different imaging systems. We show how the excellent integration of advanced biosensing techniques drives changes in the number of various encoded microspheres, enabling the accurate quantification of multitarget concentrations through the special AI decoding algorithm. Furthermore, we introduce portable optical imaging devices, including AI-integrated smartphones and portable lensless holographic microscopes, for probe visualization of encoded microspheres to facilitate rapid analysis from the laboratory to point-of-care testing (POCT). Then we outline strategies to address the challenges of future applications of AI-assisted visualized microsphere biosensors, focusing on efficient biosensing approaches with enhanced encoding capacity, lightweight smartphone-based decoding apps, and integrated systems for automated biochemical analysis. This Account aims to stimulate researchers' interest in the unique attributes of AI-assisted visualized microspheres for biochemical analysis and their corresponding encoding-decoding strategies across interdisciplinary fields, including chemistry, biology, optics, and computer science.