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<p>Lateral flow assays (LFAs) are widely used in point-of-care diagnostics due to their simplicity, portability, and rapid results. Despite their advantages, LFAs face challenges in terms of sensitivity and quantitative accuracy, particularly when relying on static end-point measurements, which capture a single time point rather than dynamic changes throughout the assay. In this study, we introduce a new approach to enhance LFA performance by integrating temporal video data and deep learning algorithms. We compare the diagnostic accuracy of static image-based analysis and video-based analysis using a smartphone-based system for human chorionic gonadotropin (hCG) detection. Temporal video data, captured in three distinct time segments (1–4, 4–7, and 7–10&#xa0;min after the reaction starts), were processed using a YOLOv8-based deep learning model, which improved diagnostic performance compared to static images. The video-based method achieved a sensitivity of 92.7%, specificity of 99.1%, and overall accuracy of 98.4% within the 1–4&#xa0;min time segment, surpassing the static image method (87.3%, 98.4%, and 97.2%, respectively). Moreover, the video approach facilitated faster results, with accurate detection in just 4&#xa0;min, compared to 15&#xa0;min used in traditional methods. These findings demonstrate that incorporating temporal video sequences into deep learning models can enhance the sensitivity, specificity, and speed of LFA-based diagnostics, offering a promising strategy for improving point-of-care testing. This approach holds potential for broader applications in clinical diagnostics, enabling faster and more accurate biomarker detection.</p> Graphical Abstract <p></p>

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Smartphone-based video deep learning enables rapid and accurate lateral flow diagnostics

  • 期刊论文

  • 发表日期：

    2025年12月22日

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  • Jia Zhu,
  • Kai Hoettges,
  • Yining Shi,
  • Fanghao Zhang,
  • Yongjie Wang,
  • Haibo Ma,
  • Eng Gee Lim,
  • Quan Zhang

摘要

Lateral flow assays (LFAs) are widely used in point-of-care diagnostics due to their simplicity, portability, and rapid results. Despite their advantages, LFAs face challenges in terms of sensitivity and quantitative accuracy, particularly when relying on static end-point measurements, which capture a single time point rather than dynamic changes throughout the assay. In this study, we introduce a new approach to enhance LFA performance by integrating temporal video data and deep learning algorithms. We compare the diagnostic accuracy of static image-based analysis and video-based analysis using a smartphone-based system for human chorionic gonadotropin (hCG) detection. Temporal video data, captured in three distinct time segments (1–4, 4–7, and 7–10 min after the reaction starts), were processed using a YOLOv8-based deep learning model, which improved diagnostic performance compared to static images. The video-based method achieved a sensitivity of 92.7%, specificity of 99.1%, and overall accuracy of 98.4% within the 1–4 min time segment, surpassing the static image method (87.3%, 98.4%, and 97.2%, respectively). Moreover, the video approach facilitated faster results, with accurate detection in just 4 min, compared to 15 min used in traditional methods. These findings demonstrate that incorporating temporal video sequences into deep learning models can enhance the sensitivity, specificity, and speed of LFA-based diagnostics, offering a promising strategy for improving point-of-care testing. This approach holds potential for broader applications in clinical diagnostics, enabling faster and more accurate biomarker detection.

Graphical Abstract