Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causing global COVID-19 pandemic since 2019 has led to increasing amount of research to study how to do fast screening and diagnosis to efficiently detect COVID-19 positive cases, and how to prevent spreading of the virus. Our research objective was to study whether SARS-CoV-2 could be detected from routine nasopharyngeal swab samples by using attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with partial least squares discriminant analysis (PLS-DA). The advantage of ATR-FTIR is that measurements can be conducted without any sample preparation and no reagents are needed. Our study included 558 positive and 558 negative samples collected from Northern Finland. Overall, we found moderate diagnostic performance for ATR-FTIR when polymerase chain reaction (PCR) was used as the gold standard: the average area under the receiver operating characteristics curve (AUROC) was 0.67-0.68 (min. 0.65, max. 0.69) with 20, 10 and 5 k-fold cross validations. Mean accuracy, sensitivity and specificity was 0.62-0.63 (min. 0.60, max. 0.65), 0.61 (min. 0.58, max. 0.65) and 0.64 (min. 0.59, max. 0.67) with 20, 10 and 5 k-fold cross validations. As a conclusion, our study with relatively large sample set clearly indicate that measured ATR-FTIR spectrum contains specific information for SARS-CoV-2 infection (P<0.001 in label permutation test). However, the diagnostic performance of ATR-FTIR remained only moderate, potentially due to low concentration of viral particles in the transport medium. Further studies are needed before ATR-FTIR can be recommended for fast screening of SARS-CoV-2 from routine nasopharyngeal swab samples.ImportanceAttenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with machine learning-based analysis was applied to detect severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) from nasopharyngeal swab samples originally collected and processed for polymerase chain reaction (PCR) analysis. Even though our results showed moderate performance, we think that our carefully designed and conducted work is valuable in the field of SARS-CoV-2 diagnostics as there were as many as 1116 nasopharyngeal swab samples (558 negative and 558 positive) collected from individual patients in a real clinical setting. The Real clinical setting refers to the fact that the nasopharyngeal swab samples were collected from people with symptoms typical for COVID-19 or asymptomatic individuals exposed to SARS-CoV-2. The presented technique could be relatively easy to use for point-of-care testing, as ATR-FTIR can be performed with a portable machine without sample preparation and machine learning-based model could give a result immediately after ATR-FTIR measurement.
In interior computed tomography (CT), the x-ray beam is collimated to a limited field-of-view (FOV) (e.g. the volume of the heart) to decrease exposure to adjacent organs, but the resulting image has a severe truncation artifact when reconstructed with traditional filtered back-projection (FBP) type algorithms. In some examinations, such as cardiac or dentomaxillofacial imaging, interior CT could be used to achieve further dose reductions. In this work, we describe a deep learning (DL) method to obtain artifact-free images from interior CT angiography. Our method employs the Pix2Pix generative adversarial network (GAN) in a two-stage process: (1) An extended sinogram is computed from a truncated sinogram with one GAN model, and (2) the FBP reconstruction obtained from that extended sinogram is used as an input to another GAN model that improves the quality of the interior reconstruction. Our double GAN (DGAN) model was trained with 10 000 truncated sinograms simulated from real computed tomography angiography slice images. Truncated sinograms (input) were used with original slice images (target) in training to yield an improved reconstruction (output). DGAN performance was compared with the adaptive de-truncation method, total variation regularization, and two reference DL methods: FBPConvNet, and U-Net-based sinogram extension (ES-UNet). Our DGAN method and ES-UNet yielded the best root-mean-squared error (RMSE) (0.03±0.01), and structural similarity index (SSIM) (0.92±0.02) values, and reference DL methods also yielded good results. Furthermore, we performed an extended FOV analysis by increasing the reconstruction area by 10% and 20%. In both cases, the DGAN approach yielded best results at RMSE (0.03±0.01 and 0.04±0.01 for the 10% and 20% cases, respectively), peak signal-to-noise ratio (PSNR) (30.5±2.6 dB and 28.6±2.6 dB), and SSIM (0.90±0.02 and 0.87±0.02). In conclusion, our method was able to not only reconstruct the interior region with improved image quality, but also extend the reconstructed FOV by 20%.
Attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy coupled with machine learning-based partial least squares discriminant analysis (PLS-DA) was applied to study if severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) could be detected from nasopharyngeal swab samples originally collected for polymerase chain reaction (PCR) analysis. Our retrospective study included 558 positive and 558 negative samples collected from Northern Finland. Overall, we found moderate diagnostic performance for ATR-FTIR when PCR analysis was used as the gold standard: the average area under the receiver operating characteristics curve (AUROC) was 0.67–0.68 (min. 0.65, max. 0.69) with 20, 10 and 5 k-fold cross validations. Mean accuracy, sensitivity and specificity was 0.62–0.63 (min. 0.60, max. 0.65), 0.61 (min. 0.58, max. 0.65) and 0.64 (min. 0.59, max. 0.67) with 20, 10 and 5 k-fold cross validations. As a conclusion, our study with relatively large sample set clearly indicate that measured ATR-FTIR spectrum contains specific information for SARS-CoV-2 infection (P < 0.001 for AUROC in label permutation test). However, the diagnostic performance of ATR-FTIR remained only moderate, potentially due to low concentration of viral particles in the transport medium. Further studies are needed before ATR-FTIR can be recommended for fast screening of SARS-CoV-2 from nasopharyngeal swab samples.
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