Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence
Bangfeng Wang,Yiwei Li,Mengfan Zhou,Yulong Han,Mingyu Zhang,Zhaolong Gao,Zetai Liu,Peng Chen,Wei Du,Jing Zhang,Xiao Jun Feng,Bi-Feng Liu +11 more
TL;DR: In this article , the authors summarize recent progress in mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms.
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Abstract: The frequent outbreak of global infectious diseases has prompted the development of rapid and effective diagnostic tools for the early screening of potential patients in point-of-care testing scenarios. With advances in mobile computing power and microfluidic technology, the smartphone-based mobile health platform has drawn significant attention from researchers developing point-of-care testing devices that integrate microfluidic optical detection with artificial intelligence analysis. In this article, we summarize recent progress in these mobile health platforms, including the aspects of microfluidic chips, imaging modalities, supporting components, and the development of software algorithms. We document the application of mobile health platforms in terms of the detection objects, including molecules, viruses, cells, and parasites. Finally, we discuss the prospects for future development of mobile health platforms.
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References
Learning from Everyday Images Enables Expert-like Diagnosis of Retinal Diseases
TL;DR: Kermany et al. report an application of a neural network trained on millions of everyday images to a database of thousands of retinal tomography images that they gathered and expert labeled, resulting in a rapid and accurate diagnosis of Retinal diseases.
Mass Surveilance of C. elegans-Smartphone-Based DIY Microscope and Machine-Learning-Based Approach for Worm Detection.
TL;DR: The suitability of smartphone-based microscopy to detect C. elegans in a complete Petri dish through a trained Histogram of Oriented Gradients feature-based Support Vector Machine is investigated.
Portable smartphone quantitation of prostate specific antigen (PSA) in a fluoropolymer microfluidic device.
TL;DR: The MCFphone has shown capable of performing rapid (13 to 22 min total assay time) colorimetric quantitative and highly sensitive fluorescence tests with good %Recovery, which represents a major step in the integration of a new generation of inexpensive and portable microfluidic devices with commercial immunoassay reagents and off-the-shelf smartphone technology.
Structural, Optical and Magnetic Properties of (Ni, Al) Co-Doped ZnO Nanoparticles
TL;DR: Pure and (Ni, Al) co-doped ZnO nanoparticles are synthesized by chemical co-precipitation method at room temperature successfully using poly ethylene glycol (PEG) as stabilizing agent to determine the properties to be found the structural, optical and magnetic.
New machine-learning technologies for computer-aided diagnosis.
Charles J. Lynch,Conor Liston +1 more
TL;DR: Machine learning can be used for computer-aided diagnosis of acute neurological events and retinal disease and can be incorporated into conventional clinical workflows to improve health outcomes.