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Rapid deep learning-assisted predictive diagnostics for point-of-care testing

Author

Listed:
  • Seungmin Lee

    (Kwangwoon University
    Korea University)

  • Jeong Soo Park

    (Kwangwoon University
    Korea University)

  • Hyowon Woo

    (Kwangwoon University)

  • Yong Kyoung Yoo

    (Catholic Kwandong University)

  • Dongho Lee

    (CALTH Inc.)

  • Seok Chung

    (Korea University)

  • Dae Sung Yoon

    (Korea University
    Korea University
    Astrion Inc)

  • Ki- Baek Lee

    (Kwangwoon University)

  • Jeong Hoon Lee

    (Kwangwoon University
    CALTH Inc.)

Abstract

Prominent techniques such as real-time polymerase chain reaction (RT-PCR), enzyme-linked immunosorbent assay (ELISA), and rapid kits are currently being explored to both enhance sensitivity and reduce assay time for diagnostic tests. Existing commercial molecular methods typically take several hours, while immunoassays can range from several hours to tens of minutes. Rapid diagnostics are crucial in Point-of-Care Testing (POCT). We propose an approach that integrates a time-series deep learning architecture and AI-based verification, for the enhanced result analysis of lateral flow assays. This approach is applicable to both infectious diseases and non-infectious biomarkers. In blind tests using clinical samples, our method achieved diagnostic times as short as 2 minutes, exceeding the accuracy of human analysis at 15 minutes. Furthermore, our technique significantly reduces assay time to just 1-2 minutes in the POCT setting. This advancement has the potential to greatly enhance POCT diagnostics, enabling both healthcare professionals and non-experts to make rapid, accurate decisions.

Suggested Citation

  • Seungmin Lee & Jeong Soo Park & Hyowon Woo & Yong Kyoung Yoo & Dongho Lee & Seok Chung & Dae Sung Yoon & Ki- Baek Lee & Jeong Hoon Lee, 2024. "Rapid deep learning-assisted predictive diagnostics for point-of-care testing," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    • Handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46069-2
    DOI: 10.1038/s41467-024-46069-2
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    as
    1. Seungmin Lee & Sunmok Kim & Dae Sung Yoon & Jeong Soo Park & Hyowon Woo & Dongho Lee & Sung-Yeon Cho & Chulmin Park & Yong Kyoung Yoo & Ki- Baek Lee & Jeong Hoon Lee, 2023. "Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. B. A. Jonsson & G. Bjornsdottir & T. E. Thorgeirsson & L. M. Ellingsen & G. Bragi Walters & D. F. Gudbjartsson & H. Stefansson & K. Stefansson & M. O. Ulfarsson, 2019. "Brain age prediction using deep learning uncovers associated sequence variants," Nature Communications, Nature, vol. 10(1), pages 1-10, December.
    3. Cheng Jin & Heng Yu & Jia Ke & Peirong Ding & Yongju Yi & Xiaofeng Jiang & Xin Duan & Jinghua Tang & Daniel T. Chang & Xiaojian Wu & Feng Gao & Ruijiang Li, 2021. "Predicting treatment response from longitudinal images using multi-task deep learning," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    4. Kevin Haan & Yijie Zhang & Jonathan E. Zuckerman & Tairan Liu & Anthony E. Sisk & Miguel F. P. Diaz & Kuang-Yu Jen & Alexander Nobori & Sofia Liou & Sarah Zhang & Rana Riahi & Yair Rivenson & W. Dean , 2021. "Deep learning-based transformation of H&E stained tissues into special stains," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
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