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

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 () and Jeong Hoon Lee ()
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Seungmin Lee: Kwangwoon University
Sunmok Kim: Kwangwoon University
Dae Sung Yoon: Korea University
Jeong Soo Park: Kwangwoon University
Hyowon Woo: Kwangwoon University
Dongho Lee: CALTH Inc.
Sung-Yeon Cho: 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

Nature Communications, 2023, vol. 14, issue 1, 1-11

Abstract: 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.

Date: 2023
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DOI: 10.1038/s41467-023-38104-5

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