Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence.
Bangfeng WangYiwei LiMengfan ZhouYulong HanMingyu ZhangZhaolong GaoZetai LiuPeng ChenWei DuXingcai ZhangXiaojun FengBi-Feng LiuPublished in: Nature communications (2023)
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.
Keyphrases
- artificial intelligence
- label free
- deep learning
- machine learning
- high throughput
- loop mediated isothermal amplification
- big data
- circulating tumor cells
- single cell
- infectious diseases
- high resolution
- real time pcr
- induced apoptosis
- newly diagnosed
- working memory
- prognostic factors
- climate change
- cell cycle arrest
- risk assessment
- cell death
- mass spectrometry
- oxidative stress
- patient reported outcomes
- endoplasmic reticulum stress
- cell proliferation
- patient reported
- plasmodium falciparum