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, Jeong Hoon Lee

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number1695
JournalNature communications
Volume15
Issue number1
DOIs
Publication statusPublished - 2024 Dec

Bibliographical note

Publisher Copyright:
© The Author(s) 2024.

ASJC Scopus subject areas

  • General Chemistry
  • General Biochemistry,Genetics and Molecular Biology
  • General Physics and Astronomy

Fingerprint

Dive into the research topics of 'Rapid deep learning-assisted predictive diagnostics for point-of-care testing'. Together they form a unique fingerprint.

Cite this