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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Computer vision meets microfluidics: a label-free method for high-throughput cell analysis
Shizheng Zhou,Bingbing Chen,Edgar S. Fu,Hong Yan +3 more
TL;DR: The use of microelectromechanical devices in combination with microfluidic chips and computer vision could enable the development of label-free, automatic, low-cost, and fast cellular information recognition and the high-throughput analysis of cellular responses to different compounds, for broad applications in fields such as drug discovery, diagnostics, and personalized medicine.
18
Microsystem Advances through Integration with Artificial Intelligence
TL;DR: In this paper , the authors summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and micro-fluidics, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine.
Handyfuge Microfluidic for On-Site Antibiotic Susceptibility Testing.
Shunji Li,Chao Wan,Bangfeng Wang,Dongjuan Chen,Wenyi Zeng,Xianzhe Hong,Lina Li,Zhengbin Pang,Wei Du,Xiao Jun Feng,Peng Chen,Yiwei Li,Bi-Feng Liu +12 more
TL;DR: In this article , an electricity-free, portable, and robust handy-fuge microfluidic chip was developed for on-site antibiotic susceptibility testing (AST), termed handyfuge-AST.
15
Precision medicine in colorectal cancer: Leveraging multi-omics, spatial omics, and artificial intelligence.
Zishan Xu,Wei Li,Xiangyang Dong,Yingying Chen,Dan Zhang,Jingnan Wang,Lin Zhou,Guoyang He +7 more
- 01 Apr 2024
TL;DR: Despite the tumor's heterogeneity and genetic and epigenetic complexity, the fusion of multi-omics, spatial omics, and AI shows the potential to overcome these challenges and advance precision medicine in CRC.
11
Deep-Learning Terahertz Single-Cell Metabolic Viability Study.
Ning Yang,Qian Shi,Mingji Wei,Yi Xiao,Muming Xia,Xiaolu Cai,Xiaodong Zhang,Wencong Wang,Xiaoqing Pan,Hanping Mao,Xiaobo Zou,Ming Guo,Xingcai Zhang +12 more
TL;DR: A cell viability assessment model based on the THz-AS (terahertz-absorption spectrum) results is established and could help visualize the cell apoptosis process for broad applications including drug screening.
10
References
U1 snRNP regulates cancer cell migration and invasion in vitro
Jung-Min Oh,Christopher C. Venters,Chao Di,Anna Maria Pinto,Lili Wan,Ihab Younis,Zhiqiang Cai,Chie Arai,Byung Ran So,Jingqi Duan,Gideon Dreyfuss +10 more
TL;DR: An unexpected role for U1 homeostasis (available U1 relative to transcription) in oncogenic and activated cell states is revealed, and U1 is suggested as a potential target for their modulation.
Review of deep learning: concepts, CNN architectures, challenges, applications, future directions
Laith Alzubaidi,Jinglan Zhang,Amjad J. Humaidi,Ayad Q. Al-Dujaili,Ye Duan,Omran Al-Shamma,José Santamaría,Mohammed A. Fadhel,Muthana Al-Amidie,Laith Farhan +9 more
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.
Fully integrated wearable sensor arrays for multiplexed in situ perspiration analysis
Wei Gao,Wei Gao,Sam Emaminejad,Sam Emaminejad,Sam Emaminejad,Hnin Yin Yin Nyein,Hnin Yin Yin Nyein,Samyuktha Challa,Kevin Chen,Kevin Chen,Austin J Peck,Hossain M. Fahad,Hossain M. Fahad,Hiroki Ota,Hiroki Ota,Hiroshi Shiraki,Hiroshi Shiraki,Daisuke Kiriya,Daisuke Kiriya,Der Hsien Lien,George A. Brooks,Ronald W. Davis,Ali Javey,Ali Javey +23 more
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.
Learning to Compare: Relation Network for Few-Shot Learning
Flood Sung,Yongxin Yang,Li Zhang,Tao Xiang,Philip H. S. Torr,Timothy M. Hospedales +5 more
- 18 Jun 2018
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.
•Posted Content
Learning to Compare: Relation Network for Few-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.