Frontiers in Sensors (Nov 2022)

AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for SARS-CoV-2 detection

  • Likun Zhang,
  • Likun Zhang,
  • Zhengyang Lei,
  • Zhengyang Lei,
  • Chufan Xiao,
  • Chufan Xiao,
  • Zhicheng Du,
  • Zhicheng Du,
  • Chenyao Jiang,
  • Chenyao Jiang,
  • Xi Yuan,
  • Xi Yuan,
  • Qiuyue Hu,
  • Qiuyue Hu,
  • Shiyao Zhai,
  • Shiyao Zhai,
  • Lulu Xu,
  • Lulu Xu,
  • Changyue Liu,
  • Changyue Liu,
  • Xiaoyun Zhong,
  • Xiaoyun Zhong,
  • Haifei Guan,
  • Haifei Guan,
  • Muhammad Hassan,
  • Muhammad Hassan,
  • Ijaz Gul,
  • Ijaz Gul,
  • Vijay Pandey,
  • Vijay Pandey,
  • Xinhui Xing,
  • Xinhui Xing,
  • Can Yang Zhang,
  • Can Yang Zhang,
  • Qian He,
  • Qian He,
  • Peiwu Qin,
  • Peiwu Qin

DOI
https://doi.org/10.3389/fsens.2022.1015223
Journal volume & issue
Vol. 3

Abstract

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Integrating artificial intelligence with SARS-CoV-2 diagnostics can help in the timely execution of pandemic control and monitoring plans. To improve the efficiency of the diagnostic process, this study aims to classify fluorescent images via traditional machine learning and deep learning-based transfer learning. A previous study reported a CRISPR-Cas13a system combined with total internal reflection fluorescence microscopy (TIRFM) to detect the existence and concentrations of SARS-CoV-2 by fluorescent images. However, the lack of professional software and excessive manual labor hinder the practicability of the system. Here, we construct a fluorescent image dataset and develop an AI-boosted CRISPR-Cas13a and total internal reflection fluorescence microscopy system for the rapid diagnosis of SARS-CoV-2. Our study proposes Fluorescent Images Classification Transfer learning based on DenseNet-121 (FICTransDense), an approach that uses TIRF images (before and after sample introduction, respectively) for preprocessing, including outlier exclusion and setting and division preprocessing (i.e., SDP). Classification results indicate that the FICTransDense and Decision Tree algorithms outperform other approaches on the SDP dataset. Most of the algorithms benefit from the proposed SDP technique in terms of Accuracy, Recall, F1 Score, and Precision. The use of AI-boosted CRISPR-Cas13a and TIRFM systems facilitates rapid monitoring and diagnosis of SARS-CoV-2.

Keywords