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
Live-cell phenotypic-biomarker microfluidic assay for the risk stratification of cancer patients via machine learning
Michael S. Manak,Jonathan S. Varsanik,Brad J. Hogan,Matthew J. Whitfield,Wendell R. Su,Nikhil Joshi,Nicolai Steinke,Andrew Min,Delaney Berger,Robert J. Saphirstein,Gauri Dixit,Thiagarajan Meyyappan,Hui-May Chu,Kevin B. Knopf,David M. Albala,Grannum R. Sant,Ashok C. Chander +16 more
TL;DR: An assay that uses machine-learning algorithms on phenotypic-biomarker data from live primary cells predicts post-surgical adverse pathology in prostate-cancer and breast cancer tissue samples from patients.
Artificial intelligence-powered microfluidics for nanomedicine and materials synthesis.
Linbo Liu,Mingcheng Bi,Yunhua Wang,Junfeng Liu,Xiwen Jiang,Zhongbin Xu,Xingcai Zhang,Xingcai Zhang +7 more
TL;DR: In this paper, the authors briefly review AI and microfluidics and introduce some applications of their combination, mainly in nanomedicine and material synthesis, and discuss the development trend of the combination of the two technologies.
69
Design and clinical validation of a point-of-care device for the diagnosis of lymphoma via contrast-enhanced microholography and machine learning.
Hyungsoon Im,Divya Pathania,Philip J. McFarland,Aliyah R. Sohani,Ismail Degani,Ismail Degani,Matthew Allen,Benjamin Coble,Benjamin Coble,Aoife Kilcoyne,Seonki Hong,Lucas Rohrer,Lucas Rohrer,Jeremy S. Abramson,Scott Dryden-Peterson,Scott Dryden-Peterson,Lioubov Fexon,Mikhail Pivovarov,Bruce A. Chabner,Hakho Lee,Cesar M. Castro,Ralph Weissleder +21 more
TL;DR: A low-cost point-of-care device that uses contrast-enhanced microholography and deep learning accurately detects aggressive lymphomas in patients referred for aspiration and biopsy of enlarged lymph nodes is reported and validated.
Deep learning of HIV field-based rapid tests.
Valérian Turbé,Carina Herbst,Thobeka Mngomezulu,Sepehr Meshkinfamfard,Nondumiso Dlamini,Thembani Mhlongo,Theresa Smit,Valeriia Cherepanova,Koki Shimada,Jobie Budd,Jobie Budd,Nestor Arsenov,Steven G. Gray,Deenan Pillay,Kobus Herbst,Maryam Shahmanesh,Rachel A. McKendry,Rachel A. McKendry +17 more
TL;DR: In this article, the authors used deep learning to classify images of rapid human immunodeficiency virus (HIV) tests acquired in rural South Africa using newly developed image capture protocols with the Samsung SM-P585 tablet.
Sub-pixel resolving optofluidic microscope for on-chip cell imaging.
Guoan Zheng,Seung Ah Lee,Samuel Yang,Changhuei Yang +3 more
TL;DR: Researchers developed a lensless, on-chip optofluidic microscope that uses pixel super-resolution to achieve sub-cellular resolution (0.75 microns) for imaging microorganisms and microspheres, enabling high-throughput, compact, and cost-effective biomedical imaging.
68