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
Plant exosome nanovesicles (PENs): green delivery platforms.
TL;DR: This review provides new ideas and methods for future research on plant exosomes, including their empowerment by artificial intelligence and gene editing, as well as their potential application in the biomedicine, food, and agriculture industries.
50
Nature-inspired micropatterns
Yunhua Wang,Guoxia Zheng,Nan Jiang,Guoliang Ying,Yiwei Li,Xiaolu Cai,Jiashen Meng,Liqiang Mai,Ming Guo,Yu Shrike Zhang,Xingcai Zhang +10 more
38
Recent advances in point-of-care testing of COVID-19.
Sungwoon Lee,Liyan Bi,Hao Chen,Dong Lin,Rongchao Mei,Yixuan Wu,Lingxin Chen,S. Joo,Jaebum Choo +8 more
TL;DR: Next-generation pandemic sensing methods incorporating artificial intelligence that can be used to meet global health needs in the future are introduced and appropriate responses of various testing devices to emerging infectious diseases and prospective preventive measures for the post-pandemic era are discussed.
33
Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions
Manish Bhaiyya,Debdatta Panigrahi,Prakash Rewatkar,Hossam Haick +3 more
TL;DR: This study reviews the integration of Machine Learning (ML) into biosensors for Point-of-Care-Testing (PoCT), enhancing diagnostic accuracy, sensitivity, and speed through ML algorithms, and explores applications in various healthcare contexts, including electrochemical and wearable sensors.
23
Artificial Intelligence−Powered Electrochemical Sensor: Recent Advances, Challenges, and Prospects
Siti Nur Ashakirin Binti Mohd Nashruddin,Faridah Hani Mohamed Salleh,Rozan Mohamad Yunus,Halimah Badioze Zaman +3 more
TL;DR: This paper reviews recent advances in AI-powered electrochemical sensors, highlighting their potential for real-time disease detection and personalized healthcare, while also discussing challenges such as data privacy, sensor stability, and algorithmic bias.
19
References
Deep Learning Enhanced Mobile-Phone Microscopy
Yair Rivenson,Hatice Ceylan Koydemir,Hongda Wang,Zhensong Wei,Zhengshuang Ren,Harun Gunaydin,Yibo Zhang,Zoltán Göröcs,Kyle Liang,Derek Tseng,Aydogan Ozcan +10 more
TL;DR: The use of deep learning is reported on to correct distortions introduced by mobile-phone-based microscopes, facilitating the production of high-resolution, denoised and colour-corrected images, matching the performance of benchtop microscopes with high-end objective lenses, also extending their limited depth-of-field.
176
Insights from nanotechnology in COVID-19 treatment.
Zhongmin Tang,Zhongmin Tang,Xingcai Zhang,Xingcai Zhang,Yiqing Shu,Ming Guo,Han Zhang,Wei Tao +7 more
TL;DR: This work focuses on SARS-CoV-2 and the detailed role that nanotechnology can play in addressing this pandemic, including i) using FDA-approved nanomaterials for drug/vaccine delivery, including further exploration of the inhalation pathway.
176
Smart cup: A minimally-instrumented, smartphone-based point-of-care molecular diagnostic device
Shih-Chuan Liao,Jing Peng,Michael G. Mauk,Sita Awasthi,Jinzhao Song,Harvey M. Friedman,Haim H. Bau,Changchun Liu +7 more
TL;DR: The smart cup is a simple, inexpensive, minimally-instrumented, smart cup platform for rapid, quantitative molecular diagnostics of pathogens at the point of care and is suitable for use at home, in the field, and in the clinic, as well as in resource-poor settings, where access to sophisticated laboratories is impractical, unaffordable, or nonexistent.
A smartphone-based chip-scale microscope using ambient illumination
Seung Ah Lee,Changhuei Yang +1 more
TL;DR: The adaptation of a smartphone's camera to function as a compact lensless microscope that allows for sub-micron resolution imaging over an ultra-wide field-of-view (FOV) and pixel super-resolution reconstruction.
A Smartphone-Based Sensing System for On-Site Quantitation of Multiple Heavy Metal Ions Using Fluorescent Carbon Nanodots-Based Microarrays.
TL;DR: This versatile and cost-effective smartphone-based sensing system featured with reliability and simplicity, is ideally suited for user- and eco- friendly point-of-need detection in resource-constrained environments.
167