IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v15y2024i1d10.1038_s41467-024-46069-2.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-024-46069-2
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-024-46069-2?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Xilin Yang & Bijie Bai & Yijie Zhang & Musa Aydin & Yuzhu Li & Sahan Yoruc Selcuk & Paloma Casteleiro Costa & Zhen Guo & Gregory A. Fishbein & Karine Atlan & William Dean Wallace & Nir Pillar & Aydoga, 2024. "Virtual birefringence imaging and histological staining of amyloid deposits in label-free tissue using autofluorescence microscopy and deep learning," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    2. Mit Shah & Marco H. A. Inácio & Chang Lu & Pierre-Raphaël Schiratti & Sean L. Zheng & Adam Clement & Antonio Marvao & Wenjia Bai & Andrew P. King & James S. Ware & Martin R. Wilkins & Johanna Mielke &, 2023. "Environmental and genetic predictors of human cardiovascular ageing," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    3. Jordi Manuello & Joosung Min & Paul McCarthy & Fidel Alfaro-Almagro & Soojin Lee & Stephen Smith & Lloyd T. Elliott & Anderson M. Winkler & Gwenaëlle Douaud, 2024. "The effects of genetic and modifiable risk factors on brain regions vulnerable to ageing and disease," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    4. Yifan Zhong & Chuang Cai & Tao Chen & Hao Gui & Jiajun Deng & Minglei Yang & Bentong Yu & Yongxiang Song & Tingting Wang & Xiwen Sun & Jingyun Shi & Yangchun Chen & Dong Xie & Chang Chen & Yunlang She, 2023. "PET/CT based cross-modal deep learning signature to predict occult nodal metastasis in lung cancer," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    5. Shu Wang & Xiaoxiang Liu & Yueying Li & Xinquan Sun & Qi Li & Yinhua She & Yixuan Xu & Xingxin Huang & Ruolan Lin & Deyong Kang & Xingfu Wang & Haohua Tu & Wenxi Liu & Feng Huang & Jianxin Chen, 2023. "A deep learning-based stripe self-correction method for stitched microscopic images," Nature Communications, Nature, vol. 14(1), pages 1-15, December.
    6. Yuan Gao & Sofia Ventura-Diaz & Xin Wang & Muzhen He & Zeyan Xu & Arlene Weir & Hong-Yu Zhou & Tianyu Zhang & Frederieke H. Duijnhoven & Luyi Han & Xiaomei Li & Anna D’Angelo & Valentina Longo & Zaiyi, 2024. "An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer," Nature Communications, Nature, vol. 15(1), pages 1-17, December.
    7. Benjamin B. Sun & Stephanie J. Loomis & Fabrizio Pizzagalli & Natalia Shatokhina & Jodie N. Painter & Christopher N. Foley & Megan E. Jensen & Donald G. McLaren & Sai Spandana Chintapalli & Alyssa H. , 2022. "Genetic map of regional sulcal morphology in the human brain from UK biobank data," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    8. Junhao Wen & Bingxin Zhao & Zhijian Yang & Guray Erus & Ioanna Skampardoni & Elizabeth Mamourian & Yuhan Cui & Gyujoon Hwang & Jingxuan Bao & Aleix Boquet-Pujadas & Zhen Zhou & Yogasudha Veturi & Mary, 2024. "The genetic architecture of multimodal human brain age," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    9. Yuzhu Li & Nir Pillar & Jingxi Li & Tairan Liu & Di Wu & Songyu Sun & Guangdong Ma & Kevin Haan & Luzhe Huang & Yijie Zhang & Sepehr Hamidi & Anatoly Urisman & Tal Keidar Haran & William Dean Wallace , 2024. "Virtual histological staining of unlabeled autopsy tissue," Nature Communications, Nature, vol. 15(1), pages 1-17, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46069-2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.