Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence

Abstract: The detection of foodborne pathogens is of great significance for safeguarding food safety and public health. In recent years, rapid detection technologies based on diverse recognition elements have advanced considerably, driven by progress in molecular biology, materials science, and information technology. This review takes recognition elements as the central theme and systematically outlines the mechanisms and research progress of antibodies, nucleic acid aptamers, nucleic acid amplification techniques, CRISPR/Cas systems, molecular imprinting technology, peptides, and small-molecule receptors in foodborne pathogen detection, while comparing their performance in terms of specificity, sensitivity, stability, and applicability. In addition, this review further elaborates on the developmental trends of detection platforms, including multi-target and multimodal integration, microfluidics combined with portable point-of-care testing (POCT) systems, and intelligent terminals empowered by artificial intelligence algorithms. These trends provide new perspectives for improving detection systems in terms of throughput, portability, and intelligence. Overall, this review aims to serve as a comprehensive reference for the development of rapid, accurate, and intelligent detection systems for foodborne pathogens.

TL;DR: Researchers developed planarized through-hole valves for a 3D reconfigurable microfluidic network, enabling pipette-free ELISA diagnostics with automated workflow, compact footprint, and comparable accuracy to traditional methods, promising improved diagnostic accessibility in resource-constrained settings.

TL;DR: In this paper, a comprehensive survey of the most important aspects of DL and including those enhancements recently added to the field is provided, and the challenges and suggested solutions to help researchers understand the existing research gaps.

TL;DR: This work bridges the technological gap between signal transduction, conditioning, processing and wireless transmission in wearable biosensors by merging plastic-based sensors that interface with the skin with silicon integrated circuits consolidated on a flexible circuit board for complex signal processing.

TL;DR: A conceptually simple, flexible, and general framework for few-shot learning, where a classifier must learn to recognise new classes given only few examples from each, which is easily extended to zero- shot learning.

TL;DR: Relation Network (RN) as mentioned in this paper learns to learn a deep distance metric to compare a small number of images within episodes, each of which is designed to simulate the few-shot setting.