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Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence

Author

Listed:
  • Bangfeng Wang

    (Huazhong University of Science and Technology)

  • Yiwei Li

    (Huazhong University of Science and Technology)

  • Mengfan Zhou

    (Huazhong University of Science and Technology)

  • Yulong Han

    (Harvard University)

  • Mingyu Zhang

    (Huazhong University of Science and Technology)

  • Zhaolong Gao

    (Huazhong University of Science and Technology)

  • Zetai Liu

    (Huazhong University of Science and Technology)

  • Peng Chen

    (Huazhong University of Science and Technology)

  • Wei Du

    (Huazhong University of Science and Technology)

  • Xingcai Zhang

    (Harvard University)

  • Xiaojun Feng

    (Huazhong University of Science and Technology)

  • Bi-Feng Liu

    (Huazhong University of Science and Technology)

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.

Suggested Citation

  • Bangfeng Wang & Yiwei Li & Mengfan Zhou & Yulong Han & Mingyu Zhang & Zhaolong Gao & Zetai Liu & Peng Chen & Wei Du & Xingcai Zhang & Xiaojun Feng & Bi-Feng Liu, 2023. "Smartphone-based platforms implementing microfluidic detection with image-based artificial intelligence," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-36017-x
    DOI: 10.1038/s41467-023-36017-x
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