Automatic diagnosis of COVID-19 with MCA-inspired TQWT-based classification of chest X-ray images.

In this era of Coronavirus disease 2019 (COVID-19), an accurate method of diagnosis with less diagnosis time and cost can effectively help in controlling the disease spread with the new variants taking birth from time to time. In order to achieve this, a two-dimensional (2D) tunable Q-wavelet transform (TQWT) based on a memristive crossbar array (MCA) is introduced in this work for the decomposition of chest X-ray images of two different datasets. TQWT has resulted in promising values of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) at the optimum values of its parameters namely quality factor (Q) of 4, and oversampling rate (r) of 3 and at a decomposition level (J) of 2. The MCA-based model is used to process decomposed images for further classification with efficient storage. These images have been further used for the classification of COVID-19 and non-COVID-19 images using ResNet50 and AlexNet convolutional neural network (CNN) models. The average accuracy values achieved for the processed chest X-ray images classification in the small and large datasets are 98.82% and 94.64%, respectively which are higher than the reported conventional methods based on different models of deep learning techniques. The average accuracy of detection of COVID-19 via the proposed method of image classification has also been achieved with less complexity, energy, power, and area consumption along with lower cost estimation as compared to CMOS-based technology.

Med-SRNet: GAN-Based Medical Image Super-Resolution via High-Resolution Representation Learning.

High-resolution (HR) medical imaging data provide more anatomical details of human body, which facilitates early-stage disease diagnosis. But it is challenging to get clear HR medical images because of the limiting factors, such as imaging systems, imaging environments, and human factors. This work presents a novel medical image super-resolution (SR) method via high-resolution representation learning based on generative adversarial network (GAN), namely, Med-SRNet. We use GAN as backbone of SR considering the advantages of GAN that can significantly reconstruct the visual quality of the images, and the high-frequency details of the images are more realistic in the image SR task. Furthermore, we employ the HR network (HRNet) in GAN generator to maintain the HR representations and repeatedly use multi-scale fusions to strengthen HR representations for facilitating SR. Moreover, we adopt deconvolution operations to recover high-quality HR representations from all the parallel lower resolution (LR) streams with the aim to yield richer aggregated features, instead of simple bilinear interpolation operations used in HRNetV2. When evaluated on a home-made medical image dataset and two public COVID-19 CT datasets, the proposed Med-SRNet outperforms other leading edge methods, which obtains higher peak signal to noise ratio (PSNR) values and structural similarity (SSIM) values, i.e., maximum improvement of 1.75 and minimum increase of 0.433 on the PSNR metric for "Brain" test sets under 8× and maximum improvement of 0.048 and minimum increase of 0.016 on the SSIM metric for "Lung" test sets under 8× compared with other methods.

DeBoNet: A deep bone suppression model ensemble to improve disease detection in chest radiographs.

Automatic detection of some pulmonary abnormalities using chest X-rays may be impacted adversely due to obscuring by bony structures like the ribs and the clavicles. Automated bone suppression methods would increase soft tissue visibility and enhance automated disease detection. We evaluate this hypothesis using a custom ensemble of convolutional neural network models, which we call DeBoNet, that suppresses bones in frontal CXRs. First, we train and evaluate variants of U-Nets, Feature Pyramid Networks, and other proposed custom models using a private collection of CXR images and their bone-suppressed counterparts. The DeBoNet, constructed using the top-3 performing models, outperformed the individual models in terms of peak signal-to-noise ratio (PSNR) (36.7977±1.6207), multi-scale structural similarity index measure (MS-SSIM) (0.9848±0.0073), and other metrics. Next, the best-performing bone-suppression model is applied to CXR images that are pooled from several sources, showing no abnormality and other findings consistent with COVID-19. The impact of bone suppression is demonstrated by evaluating the gain in performance in detecting pulmonary abnormality consistent with COVID-19 disease. We observe that the model trained on bone-suppressed CXRs (MCC: 0.9645, 95% confidence interval (0.9510, 0.9780)) significantly outperformed (p < 0.05) the model trained on non-bone-suppressed images (MCC: 0.7961, 95% confidence interval (0.7667, 0.8255)) in detecting findings consistent with COVID-19 indicating benefits derived from automatic bone suppression on disease classification. The code is available at https://github.com/sivaramakrishnan-rajaraman/Bone-Suppresion-Ensemble.

Multilevel threshold image segmentation for COVID-19 chest radiography: A framework using horizontal and vertical multiverse optimization.

COVID-19 is currently raging worldwide, with more patients being diagnosed every day. It usually is diagnosed by examining pathological photographs of the patient's lungs. There is a lot of detailed and essential information on chest radiographs, but manual processing is not as efficient or accurate. As a result, how efficiently analyzing and processing chest radiography of COVID-19 patients is an important research direction to promote COVID-19 diagnosis. To improve the processing efficiency of COVID-19 chest films, a multilevel thresholding image segmentation (MTIS) method based on an enhanced multiverse optimizer (CCMVO) is proposed. CCMVO is improved from the original Multi-Verse Optimizer by introducing horizontal and vertical search mechanisms. It has a more assertive global search ability and can jump out of the local optimum in optimization. The CCMVO-based MTIS method can obtain higher quality segmentation results than HHO, SCA, and other forms and is less prone to stagnation during the segmentation process. To verify the performance of the proposed CCMVO algorithm, CCMVO is first compared with DE, MVO, and other algorithms by 30 benchmark functions; then, the proposed CCMVO is applied to image segmentation of COVID-19 chest radiography; finally, this paper verifies that the combination of MTIS and CCMVO is very successful with good segmentation results by using the Feature Similarity Index (FSIM), the Peak Signal to Noise Ratio (PSNR), and the Structural Similarity Index (SSIM). Therefore, this research can provide an effective segmentation method for a medical organization to process COVID-19 chest radiography and then help doctors diagnose coronavirus pneumonia (COVID-19).

The effectiveness of the Safety in Interventional Radiology (SIR) Shield in reducing droplet transmission and its effect on image quality and radiation dose.

OBJECTIVE: To evaluate the efficacy of a barrier shield in reducing droplet transmission and its effect on image quality and radiation dose in an interventional suite. METHODS: A human cough droplet visualisation model in a supine position was developed to assess efficacy of barrier shield in reducing environmental contamination. Its effect on image quality (resolution and contrast) was evaluated via image quality test phantom. Changes in the radiation dose to patient post-shield utilisation was measured. RESULTS: Use of the shield prevented escape of visible fluorescent cough droplets from the containment area. No subjective change in line-pair resolution was observed. No significant difference in contrast-to-noise ratio was measured. Radiation dosage to patient was increased; this is predominantly attributed to the increased air gap and not the physical properties of the shield. CONCLUSION: Use of the barrier shield provided an effective added layer of personal protection in the interventional radiology theatre for aerosol generating procedures. ADVANCES IN KNOWLEDGE: This is the first time a human supine cough droplet visualisation has been developed. While multiple types of barrier shields have been described, this is the first systematic practical evaluation of a barrier shield designed for use in the interventional radiology theatre.

Comparison of lung image quality between CT Ark and Brilliance 64 CT during COVID-19.

AIM: This study is to compare the lung image quality between shelter hospital CT (CT Ark) and ordinary CT scans (Brilliance 64) scans. METHODS: The patients who received scans with CT Ark or Brilliance 64 CT were enrolled. Their lung images were divided into two groups according to the scanner. The objective evaluation methods of signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were used. The subjective evaluation methods including the evaluation of the fine structure under the lung window and the evaluation of the general structure under the mediastinum window were compared. Kappa method was used to assess the reliability of the subjective evaluation. The subjective evaluation results were analyzed using the Wilcoxon rank sum test. SNR and CNR were tested using independent sample t tests. RESULTS: There was no statistical difference in somatotype of enrolled subjects. The Kappa value between the two observers was between 0.68 and 0.81, indicating good consistency. For subjective evaluation results, the rank sum test P value of fine structure evaluation and general structure evaluation by the two observers was ≥ 0.05. For objective evaluation results, SNR and CNR between the two CT scanners were significantly different (P<0.05). Notably, the absolute values ​​of SNR and CNR of the CT Ark were larger than Brilliance 64 CT scanner. CONCLUSION: CT Ark is fully capable of scanning the lungs of the COVID-19 patients during the epidemic in the shelter hospital.

Automated Assessment of the Quality of Phonocardographic Recordings through Signal-to-Noise Ratio for Home Monitoring Applications.

The signal quality limits the applicability of phonocardiography at the patients' domicile. This work proposes the signal-to-noise ratio of the recorded signal as its main quality metrics. Moreover, we define the minimum acceptable values of the signal-to-noise ratio that warrantee an accuracy of the derived parameters acceptable in clinics. We considered 25 original heart sounds recordings, which we corrupted by adding noise to decrease their signal-to-noise ratio. We found that a signal-to-noise ratio equal to or higher than 14 dB warrants an uncertainty of the estimate of the valve closure latencies below 1 ms. This accuracy is higher than that required by most clinical applications. We validated the proposed method against a public database, obtaining results comparable to those obtained on our sample population. In conclusion, we defined (a) the signal-to-noise ratio of the phonocardiographic signal as the preferred metric to evaluate its quality and (b) the minimum values of the signal-to-noise ratio required to obtain an uncertainty of the latency of heart sound components compatible with clinical applications. We believe these results are crucial for the development of home monitoring systems aimed at preventing acute episodes of heart failure and that can be safely operated by naïve users.

A Study on Weak Edge Detection of COVID-19's CT Images Based on Histogram Equalization and Improved Canny Algorithm.

The coronavirus disease 2019 (COVID-19) is a substantial threat to people's lives and health due to its high infectivity and rapid spread. Computed tomography (CT) scan is one of the important auxiliary methods for the clinical diagnosis of COVID-19. However, CT image lesion edge is normally affected by pixels with uneven grayscale and isolated noise, which makes weak edge detection of the COVID-19 lesion more complicated. In order to solve this problem, an edge detection method is proposed, which combines the histogram equalization and the improved Canny algorithm. Specifically, the histogram equalization is applied to enhance image contrast. In the improved Canny algorithm, the median filter, instead of the Gaussian filter, is used to remove the isolated noise points. The K-means algorithm is applied to separate the image background and edge. And the Canny algorithm is improved continuously by combining the mathematical morphology and the maximum between class variance method (OTSU). On selecting four types of lesion images from COVID-CT date set, MSE, MAE, SNR, and the running time are applied to evaluate the performance of the proposed method. The average values of these evaluation indicators are 1.7322, 7.9010, 57.1241, and 5.4887, respectively. Compared with other three methods, these values indicate that the proposed method achieves better result. The experimental results prove that the proposed algorithm can effectively detect the weak edge of the lesion, which is helpful for the diagnosis of COVID-19.

Recovery of Ionospheric Signals Using Fully Convolutional DenseNet and Its Challenges.

The technique of active ionospheric sounding by ionosondes requires sophisticated methods for the recovery of experimental data on ionograms. In this work, we applied an advanced algorithm of deep learning for the identification and classification of signals from different ionospheric layers. We collected a dataset of 6131 manually labeled ionograms acquired from low-latitude ionosondes in Taiwan. In the ionograms, we distinguished 11 different classes of the signals according to their ionospheric layers. We developed an artificial neural network, FC-DenseNet24, based on the FC-DenseNet convolutional neural network. We also developed a double-filtering algorithm to reduce incorrectly classified signals. That made it possible to successfully recover the sporadic E layer and the F2 layer from highly noise-contaminated ionograms whose mean signal-to-noise ratio was low, SNR = 1.43. The Intersection over Union (IoU) of the recovery of these two signal classes was greater than 0.6, which was higher than the previous models reported. We also identified three factors that can lower the recovery accuracy: (1) smaller statistics of samples; (2) mixing and overlapping of different signals; (3) the compact shape of signals.

Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques.

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door-antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

Label-free detection of virus-like particles employing rotationally symmetric nanowire array based whispering gallery and quasi-whispering gallery resonant modes onto a silicon platform.

In spite of tremendous advancements in modern diagnostics, there is a dire need for reliable, label-free detection of highly contagious pathogens like viruses. In view of the limitations of existing diagnostic techniques, the present theoretical study proposes a novel scheme of detecting virus-like particles employing whispering gallery and quasi-whispering gallery resonant modes of a composite optical system. Whereas whispering gallery mode (WGM) resonators are conventionally realized using micro-disk, -ring, -toroid or spherical structures, the present study utilizes a rotationally symmetric array of silicon nanowires which offers higher sensitivity compared to the conventional WGM resonator while detecting virus-like particles. Notwithstanding the relatively low quality factor of the system, the underlying multiple-scattering mediated photon entrapment, coupled with peripheral total-internal reflection, results in high fidelity of the system against low signal-to-noise ratio. Finite difference time domain based numerical analysis has been performed to correlate resonant modes of the array with spatial location of the virus. The correlation has been subsequently utilized for statistical analysis of simulated test cases. Assuming detection to be limited by resolution of the measurement system, results of the analysis suggest that for only about 5% of the simulate test cases the resonant wavelength shift lies within the minimum detection range of 0.001-0.01 nm. For a single virus of 160 nm diameter, more than 8 nm shift of the resonant mode and nearly 100% change of quality factor are attained with the proposed nanowire array based photonic structure.

Influence of surgical and N95 face masks on speech perception and listening effort in noise.

Daily-life conversation relies on speech perception in quiet and noise. Because of the COVID-19 pandemic, face masks have become mandatory in many situations. Acoustic attenuation of sound pressure by the mask tissue reduces speech perception ability, especially in noisy situations. Masks also can impede the process of speech comprehension by concealing the movements of the mouth, interfering with lip reading. In this prospective observational, cross-sectional study including 17 participants with normal hearing, we measured the influence of acoustic attenuation caused by medical face masks (mouth and nose protection) according to EN 14683 and of N95 masks according to EN 1149 (EN 14683) on the speech recognition threshold and listening effort in various types of background noise. Averaged over all noise signals, a surgical mask significantly reduced the speech perception threshold in noise was by 1.6 dB (95% confidence interval [CI], 1.0, 2.1) and an N95 mask reduced it significantly by 2.7 dB (95% CI, 2.2, 3.2). Use of a surgical mask did not significantly increase the 50% listening effort signal-to-noise ratio (increase of 0.58 dB; 95% CI, 0.4, 1.5), but use of an N95 mask did so significantly, by 2.2 dB (95% CI, 1.2, 3.1). In acoustic measures, mask tissue reduced amplitudes by up to 8 dB at frequencies above 1 kHz, whereas no reduction was observed below 1 kHz. We conclude that face masks reduce speech perception and increase listening effort in different noise signals. Together with additional interference because of impeded lip reading, the compound effect of face masks could have a relevant impact on daily life communication even in those with normal hearing.

Neutrophil-to-lymphocyte ratio, platelets-to-lymphocyte ratio, and eosinophils correlation with high-resolution computer tomography severity score in COVID-19 patients.

Inflammation has an important role in the progression of various viral pneumonia, including COVID-19. Circulating biomarkers that can evaluate inflammation and immune status are potentially useful in diagnosing and prognosis of COVID-19 patients. Even more so when they are a part of the routine evaluation, chest CT could have even higher diagnostic accuracy than RT-PCT alone in a suggestive clinical context. This study aims to evaluate the correlation between inflammatory markers such as neutrophil-to-lymphocyte ratio (NLR), platelets-to-lymphocytes ratio (PLR), and eosinophils with the severity of CT lesions in patients with COVID-19. The second objective was to seek a statically significant cut-off value for NLR and PLR that could suggest COVID-19. Correlation of both NLR and PLR with already established inflammatory markers such as CRP, ESR, and those specific for COVID-19 (ferritin, D-dimers, and eosinophils) were also evaluated. One hundred forty-nine patients with confirmed COVID-19 disease and 149 age-matched control were evaluated through blood tests, and COVID-19 patients had thorax CT performed. Both NLR and PLR correlated positive chest CT scan severity. Both NLR and PLR correlated positive chest CT scan severity. When NLR value is below 5.04, CT score is lower than 3 with a probability of 94%, while when NLR is higher than 5.04, the probability of severe CT changes is only 50%. For eosinophils, a value of 0.35% corresponds to chest CT severity of 2 (Se = 0.88, Sp = 0.43, AUC = 0.661, 95% CI (0.544; 0.779), p = 0.021. NLR and PLR had significantly higher values in COVID-19 patients. In our study a NLR = 2.90 and PLR = 186 have a good specificity (0.89, p = 0.001, respectively 0.92, p<0.001). Higher levels in NLR, PLR should prompt the clinician to prescribe a thorax CT as it could reveal important lesions that could influence the patient's future management.

Parameter estimation from ICC curves.

Incidence vs. Cumulative Cases (ICC) curves are introduced and shown to provide a simple framework for parameter identification in the case of the most elementary epidemiological model, consisting of susceptible, infected, and removed compartments. This novel methodology is used to estimate the basic reproduction ratio of recent outbreaks, including those associated with the ongoing COVID-19 pandemic.

Total-Body PET Imaging in Infectious Diseases.

Total-body PET enables high-sensitivity imaging with dramatically improved signal-to-noise ratio. These enhanced performance characteristics allow for decreased PET scanning times acquiring data "total-body wide" and can be leveraged to decrease the amount of radiotracer required, thereby permitting more frequent imaging or longer imaging periods during radiotracer decay. Novel approaches to PET imaging of infectious diseases are emerging, including those that directly visualize pathogens in vivo and characterize concomitant immune responses and inflammation. Efforts to develop these imaging approaches are hampered by challenges of traditional imaging platforms, which may be overcome by novel total-body PET strategies.

Cardiovascular thrombotic complications in acute ischemic stroke assessed by chest spectral computed tomography during COVID-19.

During the pandemic context, diagnostic algorithms had to be adapted considering the decimated medical personnel, local technical resources, and the likelihood of contamination. Given the higher probability of thrombotic complications related to COVID-19 and the availability of a dual-layer spectral computed tomography (CT) scanner, we have recently adopted the use of low-dose, non-gated, chest CT scans performed five minutes after contrast administration among patients admitted with acute ischemic stroke (AIS) undergoing cerebrovascular CT angiography. Dual-layer spectral CT comprises a single X-ray source and two-layer detector with different photon-absorption capabilities. In addition to conventional images, the two distinct energy datasets obtained enable multiparametric spectral analysis without need to change the original scanning protocol. The two spectral features that emerge as most useful for patients with AIS are virtual monoenergetic imaging and iodine-based results. Aside from the evaluation of lung parenchyma, this novel strategy enables ruling out cardioembolic sources and simultaneously providing evidence of pulmonary and myocardial injury in a single session and immediately after CT cerebrovascular angiography. Furthermore, it involves a non-invasive, seemingly accurate, unsophisticated, safer (very low radiation dose and no contrast administration), and cheaper tool for ruling out cardioembolic sources compared to transesophageal echocardiogram and cardiac CT. Accordingly, we sought to standardize the technical aspects and overview the usefulness of delayed-phase, low-dose chest spectral CT in patients admitted with AIS.

Guidelines on Setting Up Stations for Remote Viewing of Nuclear Medicine and Molecular Imaging Studies During COVID-19.

The current pandemic has created a situation where nuclear medicine practitioners and medical physicists read or process nuclear medicine images remotely from their home office. This article presents recommendations on the components and specifications when setting up a remote viewing station for nuclear medicine imaging.

In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19.

Rapid generation of diagnostics is paramount to understand epidemiology and to control the spread of emerging infectious diseases such as COVID-19. Computational methods to predict serodiagnostic epitopes that are specific for the pathogen could help accelerate the development of new diagnostics. A systematic survey of 27 SARS-CoV-2 proteins was conducted to assess whether existing B-cell epitope prediction methods, combined with comprehensive mining of sequence databases and structural data, could predict whether a particular protein would be suitable for serodiagnosis. Nine of the predictions were validated with recombinant SARS-CoV-2 proteins in the ELISA format using plasma and sera from patients with SARS-CoV-2 infection, and a further 11 predictions were compared to the recent literature. Results appeared to be in agreement with 12 of the predictions, in disagreement with 3, while a further 5 were deemed inconclusive. We showed that two of our top five candidates, the N-terminal fragment of the nucleoprotein and the receptor-binding domain of the spike protein, have the highest sensitivity and specificity and signal-to-noise ratio for detecting COVID-19 sera/plasma by ELISA. Mixing the two antigens together for coating ELISA plates led to a sensitivity of 94% (N = 80 samples from persons with RT-PCR confirmed SARS-CoV-2 infection), and a specificity of 97.2% (N = 106 control samples).

Fluorescence polarization system for rapid COVID-19 diagnosis.

Prompt diagnosis, patient isolation, and contact tracing are key measures to contain the coronavirus disease 2019 (COVID-19). Molecular tests are the current gold standard for COVID-19 detection, but are carried out at central laboratories, delaying treatment and control decisions. Here we describe a portable assay system for rapid, onsite COVID-19 diagnosis. Termed CODA (CRISPR Optical Detection of Anisotropy), the method combined isothermal nucleic acid amplification, activation of CRISPR/Cas12a, and signal generation in a single assay, eliminating extra manual steps. Importantly, signal detection was based on the ratiometric measurement of fluorescent anisotropy, which allowed CODA to achieve a high signal-to-noise ratio. For point-of-care operation, we built a compact, standalone CODA device integrating optoelectronics, an embedded heater, and a microcontroller for data processing. The developed system completed SARS-CoV-2 RNA detection within 20 min of sample loading; the limit of detection reached 3 copy/µL. When applied to clinical samples (10 confirmed COVID-19 patients; 10 controls), the rapid CODA test accurately classified COVID-19 status, in concordance with gold-standard clinical diagnostics.

Ultrarapid On-Site Detection of SARS-CoV-2 Infection Using Simple ATR-FTIR Spectroscopy and an Analysis Algorithm: High Sensitivity and Specificity.

There is an urgent need for ultrarapid testing regimens to detect the severe acute respiratory syndrome coronavirus 2 [SARS-CoV-2] infections in real-time within seconds to stop its spread. Current testing approaches for this RNA virus focus primarily on diagnosis by RT-qPCR, which is time-consuming, costly, often inaccurate, and impractical for general population rollout due to the need for laboratory processing. The latency until the test result arrives with the patient has led to further virus spread. Furthermore, latest antigen rapid tests still require 15-30 min processing time and are challenging to handle. Despite increased polymerase chain reaction (PCR)-test and antigen-test efforts, the pandemic continues to evolve worldwide. Herein, we developed a superfast, reagent-free, and nondestructive approach of attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy with subsequent chemometric analysis toward the prescreening of virus-infected samples. Contrived saliva samples spiked with inactivated γ-irradiated COVID-19 virus particles at levels down to 1582 copies/mL generated infrared (IR) spectra with a good signal-to-noise ratio. Predominant virus spectral peaks are tentatively associated with nucleic acid bands, including RNA. At low copy numbers, the presence of a virus particle was found to be capable of modifying the IR spectral signature of saliva, again with discriminating wavenumbers primarily associated with RNA. Discrimination was also achievable following ATR-FTIR spectral analysis of swabs immersed in saliva variously spiked with virus. Next, we nested our test system in a clinical setting wherein participants were recruited to provide demographic details, symptoms, parallel RT-qPCR testing, and the acquisition of pharyngeal swabs for ATR-FTIR spectral analysis. Initial categorization of swab samples into negative versus positive COVID-19 infection was based on symptoms and PCR results (n = 111 negatives and 70 positives). Following training and validation (using n = 61 negatives and 20 positives) of a genetic algorithm-linear discriminant analysis (GA-LDA) algorithm, a blind sensitivity of 95% and specificity of 89% was achieved. This prompt approach generates results within 2 min and is applicable in areas with increased people traffic that require sudden test results such as airports, events, or gate controls.