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

Seungmin Lee, Jeong Soo Park, Hyowon Woo, Yong Kyoung Yoo, Dongho Lee, Seok Chung, Dae Sung Yoon, Ki- Baek Lee and Jeong Hoon Lee ()
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Seungmin Lee: Kwangwoon University
Jeong Soo Park: Kwangwoon University
Hyowon Woo: Kwangwoon University
Yong Kyoung Yoo: Catholic Kwandong University
Dongho Lee: CALTH Inc.
Seok Chung: Korea University
Dae Sung Yoon: Korea University
Ki- Baek Lee: Kwangwoon University
Jeong Hoon Lee: Kwangwoon University

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-46069-2

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