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Sample-to-answer platform for the clinical evaluation of COVID-19 using a deep learning-assisted smartphone-based assay

Author

Listed:
  • Seungmin Lee

    (Kwangwoon University
    Korea University)

  • Sunmok Kim

    (Kwangwoon University)

  • Dae Sung Yoon

    (Korea University
    Korea University
    Astrion Inc)

  • Jeong Soo Park

    (Kwangwoon University)

  • Hyowon Woo

    (Kwangwoon University)

  • Dongho Lee

    (CALTH Inc.)

  • Sung-Yeon Cho

    (The Catholic University of Korea
    The Catholic University of Korea)

  • Chulmin Park

    (The Catholic University of Korea)

  • Yong Kyoung Yoo

    (Catholic Kwandong University)

  • Ki- Baek Lee

    (Kwangwoon University)

  • Jeong Hoon Lee

    (Kwangwoon University)

Abstract

Since many lateral flow assays (LFA) are tested daily, the improvement in accuracy can greatly impact individual patient care and public health. However, current self-testing for COVID-19 detection suffers from low accuracy, mainly due to the LFA sensitivity and reading ambiguities. Here, we present deep learning-assisted smartphone-based LFA (SMARTAI-LFA) diagnostics to provide accurate decisions with higher sensitivity. Combining clinical data learning and two-step algorithms enables a cradle-free on-site assay with higher accuracy than the untrained individuals and human experts via blind tests of clinical data (n = 1500). We acquired 98% accuracy across 135 smartphone application-based clinical tests with different users/smartphones. Furthermore, with more low-titer tests, we observed that the accuracy of SMARTAI-LFA was maintained at over 99% while there was a significant decrease in human accuracy, indicating the reliable performance of SMARTAI-LFA. We envision a smartphone-based SMARTAI-LFA that allows continuously enhanced performance by adding clinical tests and satisfies the new criterion for digitalized real-time diagnostics.

Suggested Citation

  • 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.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-38104-5
    DOI: 10.1038/s41467-023-38104-5
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    Cited by:

    1. 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.

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