Exhaled VOCs can discriminate subjects with COVID-19 from healthy controls

Clinical applications of volatilomic assays

The study of metabolomics is revealing immense potential for diagnosis, therapy monitoring, and understanding of pathogenesis processes. Volatilomics is a subcategory of metabolomics interested in the detection of molecules that are small enough to be released in the gas phase. Volatile compounds produced by cellular processes are released into the blood and lymph, and can reach the external environment through different pathways, such as the blood-air interface in the lung that are detected in breath, or the blood-water interface in the kidney that leads to volatile compounds detected in urine. Besides breath and urine, additional sources of volatile compounds such as saliva, blood, feces, and skin are available. Volatilomics traces its roots back over fifty years to the pioneering investigations in the 1970s. Despite extensive research, the field remains in its infancy, hindered by a lack of standardization despite ample experimental evidence. The proliferation of analytical instrumentations, sample preparations and methods of volatilome sampling still make it difficult to compare results from different studies and to establish a common standard approach to volatilomics. This review aims to provide an overview of volatilomics' diagnostic potential, focusing on two key technical aspects: sampling and analysis. Sampling poses a challenge due to the susceptibility of human samples to contamination and confounding factors from various sources like the environment and lifestyle. The discussion then delves into targeted and untargeted approaches in volatilomics. Some case studies are presented to exemplify the results obtained so far. Finally, the review concludes with a discussion on the necessary steps to fully integrate volatilomics into clinical practice.

A Review on Long COVID Screening: Challenges and Perspectives Focusing on Exhaled Breath Gas Sensing

Long COVID (LC) is a great global health concern, affecting individuals recovering from SARS-CoV-2 infection. The persistent and varied symptoms across multiple organs complicate diagnosis and management, and an incomplete understanding of the condition hinders advancements in therapeutics. Current diagnostic methods face challenges related to standardization and completeness. To overcome this, new technologies such as sensor-based electronic noses are being explored for LC assessment, offering a noninvasive screening approach via volatile organic compounds (VOC) sensing in exhaled breath. Although specific LC-associated VOCs have not been fully characterized, insights from COVID-19 research suggest their potential as biomarkers. Additionally, AI-driven chemometrics are promising in identifying and predicting outcomes; despite challenges, AI-driven technologies hold the potential to enhance LC evaluation, providing rapid and accurate diagnostics for improved patient care and outcomes. This review underscores the importance of emerging and sensing technologies and comprehensive diagnostic strategies to address screening and treatment challenges in the face of LC.

Analysis of the Urine Volatilome of COVID-19 Patients and the Possible Metabolic Alterations Produced by the Disease

Alterations in metabolism caused by SARS-CoV-2 infection have been highlighted in various investigations and have been used to search for biomarkers in different biological matrices. However, the selected biomarkers vary greatly across studies. Our objective is to provide a robust selection of biomarkers, including results from different sample treatments in the analysis of volatile organic compounds (VOCs) present in urine samples from patients with COVID-19. Between September 2021 and May 2022, urine samples were collected from 35 hospitalized COVID-19 patients and 32 healthy controls. The samples were analyzed by headspace (HS) solid phase microextraction (SPME) coupled to gas chromatography-mass spectrometry (GC-MS). Analyses were conducted on untreated urine samples and on samples that underwent specific pretreatments: lyophilization and treatment with sulfuric acid. Partial Least Squares Linear Discriminant Analysis (PLS-LDA) and Subwindow Permutation Analysis (SPA) models were established to distinguish patterns between COVID-19 patients and healthy controls. The results identify compounds that are present in different proportions in urine samples from COVID-19 patients compared to those from healthy individuals. Analysis of urine samples using HS-SPME-GC-MS reveals differences between COVID-19 patients and healthy individuals. These differences are more pronounced when methods that enhance VOC formation are used. However, these pretreatments can cause reactions between sample components, creating additional products or removing compounds, so biomarker selection could be altered. Therefore, using a combination of methods may be more informative when evaluating metabolic alterations caused by viral infections and would allow for a better selection of biomarkers.

Canine olfactory detection and its relevance for the medical identification of patients with COVID-19

INTRODUCTION

The assessment of Volatile Organic Compounds (VOCs) in exhaled breath or sweat represents a potential non-invasive and rapid diagnostic tool for respiratory diseases.

OBJECTIVE

To determine if trained dogs can reliably identify the odour associated with COVID19.

METHODS

This is a monocentric prospective study carried out in the Emergency Department (ED) of a university hospital fromJulyto November 2021.Axillary sweat samples from all patients were collected bytwo trained health care professionals. The samples were collected in the form of sterile gauze swabs placed under the armpits for at least 4 h for each patient.Then, Tubes wereshiftedto the double-blind dog training centre for VOC detection by two individuals.

RESULTS

Dogs were tested using a total of 129 axillary sweat samples; 69 of the 107 patients who tested positive for COVID-19 based on their odours had a positive PCR/Antigen test and 19 of the 22 patients who were tested negative for COVID-19 by the dogs had a negative PCR test. The sniffer dog infection detection method had a sensitivity of 95.83% and a specificity of 33.33%. The PPV was 64.49% and the NPVwas 86.36%. The measurement of the intensity of the connection between the two variables (disease/sign) was very strong (Q = 0.84). This link is statistically significant (X2 = 19.13) with a probability p ≤ 0.001.

CONCLUSION

Overall, the use of trained detection dogs as a screening method for SARS-CoV-2 is an interesting avenue of research that warrants further exploration and validation.

Exhaled Volatile Organic Compounds Detection in Pneumonia Screening: A Comprehensive Meta-analysis

BACKGROUND

Pneumonia is a common lower respiratory tract infection, and early diagnosis is crucial for timely treatment and improved prognosis. Traditional diagnostic methods for pneumonia, such as chest imaging and microbiological examinations, have certain limitations. Exhaled volatile organic compounds (VOCs) detection, as an emerging non-invasive diagnostic technique, has shown potential application value in pneumonia screening.

METHOD

A systematic search was conducted on PubMed, Embase, Cochrane Library, and Web of Science, with the retrieval time up to March 2024. The inclusion criteria were diagnostic studies evaluating exhaled VOCs detection for the diagnosis of pneumonia, regardless of the trial design type. A meta-analysis was performed using a bivariate model for sensitivity and specificity.

RESULTS

A total of 14 diagnostic studies were included in this meta-analysis. The pooled results demonstrated that exhaled VOCs detection had a combined sensitivity of 0.94 (95% CI: 0.92-0.95) and a combined specificity of 0.83 (95% CI: 0.81-0.84) in pneumonia screening, with an area under the summary receiver operating characteristic (SROC) curve (AUC) of 0.96. The pooled diagnostic odds ratio (DOR) was 104.37 (95% CI: 27.93-390.03), and the pooled positive and negative likelihood ratios (LR) were 8.98 (95% CI: 3.88-20.80) and 0.11 (95% CI: 0.05-0.22), indicating a high diagnostic performance.

CONCLUSION

This study highlights the potential of exhaled VOCs detection as an effective, non-invasive screening method for pneumonia, which could facilitate future diagnosis in pneumonia. Further high-quality, large-scale studies are required to confirm the clinical utility of exhaled VOCs detection in pneumonia screening.

STUDY REGISTRATION

PROSPERO, Review no. CRD42024520498.

Establishing Healthy Breath Baselines With Tin Oxide Sensors: Fundamental Building Blocks for Noninvasive Health Monitoring

INTRODUCTION

Volatile organic compounds (VOCs) in breath serve as a source of biomarkers for medical conditions relevant to warfighter health including Corona Virus Disease and other potential biological threats. Electronic noses are integrated arrays of gas sensors that are cost-effective and miniaturized devices that rapidly respond to VOCs in exhaled breath. The current study seeks to qualify healthy breath baselines of exhaled VOC profiles through analysis using a commercialized array of metal oxide (MOX) sensors.

MATERIALS AND METHODS

Subjects were recruited/consented through word of mouth and using posters. For each sample, breath was analyzed using an array of MOX sensors with parameters that were previously established. Data were also collected using a lifestyle questionnaire and from a blood test to assess markers of general health. Sensor data were processed using a feature extraction algorithm, which were analyzed through statistical approaches to identify correlations with confounding factors. Reproducibility was also assessed through relative standard deviation values of sensor features within a single subject and between different volunteers.

RESULTS

A total of 164 breath samples were collected from different individuals, and 10 of these volunteers provided an additional 9 samples over 6 months for the longitudinal study. First, data from different subjects were analyzed, and the trends of the 17 extracted features were elucidated. This revealed not only a high degree of correlation between sensors within the array but also between some of the features extracted within a single sensor. This helped guide the removal of multicollinear features for multivariate statistical analyses. No correlations were identified between sensor features and confounding factors of interest (age, body mass index, smoking, and sex) after P-value adjustment, indicating that these variables have an insignificant impact on the observed sensor signal. Finally, the longitudinal replicates were analyzed, and reproducibility assessment showed that the variability between subjects was significantly higher than within replicates of a single volunteer (P-value = .002). Multivariate analyses within the longitudinal data displayed that subjects could not be distinguished from one another, indicating that there may be a universal healthy breath baseline that is not specific to particular individuals.

CONCLUSIONS

The current study sought to qualify healthy baselines of VOCs in exhaled breath using a MOX sensor array that can be leveraged in the future to detect medical conditions relevant to warfighter health. For example, the results of the study will be useful, as the healthy breath VOC data from the sensor array can be cross-referenced in future studies aiming to use the device to distinguish disease states. Ultimately, the sensors may be integrated into a portable breathalyzer or current military gear to increase warfighter readiness through rapid and noninvasive health monitoring.

A comprehensive meta-analysis and systematic review of breath analysis in detection of COVID-19 through Volatile organic compounds

BACKGROUND

The COVID-19 pandemic had profound global impacts on daily lives, economic stability, and healthcare systems. Diagnosis of COVID-19 infection via RT-PCR was crucial in reducing spread of disease and informing treatment management. While RT-PCR is a key diagnostic test, there is room for improvement in the development of diagnostic criteria. Identification of volatile organic compounds (VOCs) in exhaled breath provides a fast, reliable, and economically favorable alternative for disease detection.

METHODS

This meta-analysis analyzed the diagnostic performance of VOC-based breath analysis in detection of COVID-19 infection. A systematic review of twenty-nine papers using the grading criteria from Newcastle-Ottawa Scale (NOS) and PRISMA guidelines was conducted.

RESULTS

The cumulative results showed a sensitivity of 0.92 (95 % CI, 90 %-95 %) and a specificity of 0.90 (95 % CI 87 %-93 %). Subgroup analysis by variant demonstrated strong sensitivity to the original strain compared to the Omicron and Delta variant in detection of SARS-CoV-2 infection. An additional subgroup analysis of detection methods showed eNose technology had the highest sensitivity when compared to GC-MS, GC-IMS, and high sensitivity-MS.

CONCLUSION

Overall, these results support the use of breath analysis as a new detection method of COVID-19 infection.

Quantifying exhaled acetone and isoprene through solid phase microextraction and gas chromatography-mass spectrometry

BACKGROUND

Acetone, isoprene, and other volatile organic compounds (VOCs) in exhaled breath have been shown to be biomarkers for many medical conditions. Researchers use different techniques for VOC detection, including solid phase microextraction (SPME), to preconcentrate volatile analytes prior to instrumental analysis by gas chromatography-mass spectrometry (GC-MS). These techniques include a previously developed method to detect VOCs in breath directly using SPME, but it is uncommon for studies to quantify exhaled volatiles because it can be time consuming due to the need of many external/internal standards, and there is no standardized or widely accepted method. The objective of this study was to develop an accessible method to quantify acetone and isoprene in breath by SPME GC-MS.

RESULTS

A system was developed to mimic human exhalation and expose VOCs to a SPME fiber in the gas phase at known concentrations. VOCs were bubbled/diluted with dry air at a fixed flow rate, duration, and volume that was comparable to a previously developed breath sampling method. Identification of acetone and isoprene through GC-MS was verified using standards and observing overlaps in chromatographic retention/mass spectral fragmentation. Calibration curves were developed for these two analytes, which showed a high degree of linear correlation. Acetone and isoprene displayed limits of detection/quantification equal to 12 ppb/37 ppb and 73 ppb/222 ppb respectively. Quantification results in healthy breath samples (n = 15) showed acetone concentrations spanned between 71 ppb and 294 ppb, and isoprene varied between 170 ppb and 990 ppb. Both concentration ranges for acetone and isoprene in this study overlap with those reported in existing literature.

SIGNIFICANCE

Results indicate the development of a system to quantify acetone and isoprene in breath that can be adapted to diverse sampling methods and instrumental analyses beyond SPME GC-MS.

GC-MS-based metabolomics of volatile organic compounds in exhaled breath: applications in health and disease. A review

Exhaled breath analysis, with particular emphasis on volatile organic compounds, represents a growing area of clinical research due to its obvious advantages over other diagnostic tests. Numerous pathologies have been extensively investigated for the identification of specific biomarkers in exhalates through metabolomics. However, the transference of breath tests to clinics remains limited, mainly due to deficiency in methodological standardization. Critical steps include the selection of breath sample types, collection devices, and enrichment techniques. GC-MS is the reference analytical technique for the analysis of volatile organic compounds in exhalates, especially during the biomarker discovery phase in metabolomics. This review comprehensively examines and compares metabolomic studies focusing on cancer, lung diseases, and infectious diseases. In addition to delving into the experimental designs reported, it also provides a critical discussion of the methodological aspects, ranging from the experimental design and sample collection to the identification of potential pathology-specific biomarkers.

Canine olfactory detection of SARS-CoV-2-infected humans-a systematic review

PURPOSE

To complement conventional testing methods for severe acute respiratory syndrome coronavirus type 2 infections, dogs' olfactory capability for true real-time detection has been investigated worldwide. Diseases produce specific scents in affected individuals via volatile organic compounds. This systematic review evaluates the current evidence for canine olfaction as a reliable coronavirus disease 2019 screening tool.

METHODS

Two independent study quality assessment tools were used: the QUADAS-2 tool for the evaluation of laboratory tests' diagnostic accuracy, designed for systematic reviews, and a general evaluation tool for canine detection studies, adapted to medical detection. Various study design, sample, dog, and olfactory training features were considered as potential confounding factors.

RESULTS

Twenty-seven studies from 15 countries were evaluated. Respectively, four and six studies had a low risk of bias and high quality: the four QUADAS-2 nonbiased studies resulted in ranges of 81%-97% sensitivity and 91%-100% specificity. The six high-quality studies, according to the general evaluation system, revealed ranges of 82%-97% sensitivity and 83%-100% specificity. The other studies contained high bias risks and applicability and/or quality concerns.

CONCLUSIONS

Standardization and certification procedures as used for canine explosives detection are needed for medical detection dogs for the optimal and structured usage of their undoubtful potential.

Methods to Detect Volatile Organic Compounds for Breath Biopsy Using Solid-Phase Microextraction and Gas Chromatography-Mass Spectrometry

Volatile organic compounds (VOCs) are byproducts from metabolic pathways that can be detected in exhaled breath and have been reported as biomarkers for different diseases. The gold standard for analysis is gas chromatography-mass spectrometry (GC-MS), which can be coupled with various sampling methods. The current study aims to develop and compare different methods for sampling and preconcentrating VOCs using solid-phase microextraction (SPME). An in-house sampling method, direct-breath SPME (DB-SPME), was developed to directly extract VOCs from breath using a SPME fiber. The method was optimized by exploring different SPME types, the overall exhalation volume, and breath fractionation. DB-SPME was quantitatively compared to two alternative methods involving the collection of breath in a Tedlar bag. In one method, VOCs were directly extracted from the Tedlar bag (Tedlar-SPME) and in the other, the VOCs were cryothermally transferred from the Tedlar bag to a headspace vial (cryotransfer). The methods were verified and quantitatively compared using breath samples (n = 15 for each method respectively) analyzed by GC-MS quadrupole time-of-flight (QTOF) for compounds including but not limited to acetone, isoprene, toluene, limonene, and pinene. The cryotransfer method was the most sensitive, demonstrating the strongest signal for the majority of the VOCs detected in the exhaled breath samples. However, VOCs with low molecular weights, including acetone and isoprene, were detected with the highest sensitivity using the Tedlar-SPME. On the other hand, the DB-SPME was less sensitive, although it was rapid and had the lowest background GC-MS signal. Overall, the three breath-sampling methods can detect a wide variety of VOCs in breath. The cryotransfer method may be optimal when collecting a large number of samples using Tedlar bags, as it allows the long-term storage of VOCs at low temperatures (-80 °C), while Tedlar-SPME may be more effective when targeting relatively small VOCs. The DB-SPME method may be the most efficient when more immediate analyses and results are required.

Diagnostic performance of eNose technology in COVID-19 patients after hospitalization

BACKGROUND

Volatile organic compounds (VOCs) produced by human cells reflect metabolic and pathophysiological processes which can be detected with the use of electronic nose (eNose) technology. Analysis of exhaled breath may potentially play an important role in diagnosing COVID-19 and stratification of patients based on pulmonary function or chest CT.

METHODS

Breath profiles of COVID-19 patients were collected with an eNose device (SpiroNose) 3 months after discharge from the Leiden University Medical Centre and matched with breath profiles from healthy individuals for analysis. Principal component analysis was performed with leave-one-out cross validation and visualised with receiver operating characteristics. COVID-19 patients were stratified in subgroups with a normal pulmonary diffusion capacity versus patients with an impaired pulmonary diffusion capacity (DLCOc < 80% of predicted) and in subgroups with a normal chest CT versus patients with COVID-19 related chest CT abnormalities.

RESULTS

The breath profiles of 135 COVID-19 patients were analysed and matched with 174 healthy controls. The SpiroNose differentiated between COVID-19 after hospitalization and healthy controls with an AUC of 0.893 (95-CI, 0.851-0.934). There was no difference in VOCs patterns in subgroups of COVID-19 patients based on diffusion capacity or chest CT.

CONCLUSIONS

COVID-19 patients have a breath profile distinguishable from healthy individuals shortly after hospitalization which can be detected using eNose technology. This may suggest ongoing inflammation or a common repair mechanism. The eNose could not differentiate between subgroups of COVID-19 patients based on pulmonary diffusion capacity or chest CT.

Simultaneous Sensing of Multiplex Volatile Organic Compounds by Adsorption and Plasmon Dual-Induced Raman Enhancement Technique

Developing highly efficient gas sensors with excellent performance for rapid and sensitive detection of volatile organic compounds (VOCs) is of critical importance for the protection of human health, ecological environment, and other factors. Here, a robust gas sensor based on Raman technology was constructed by an in situ grown 2D covalent organic framework (COF) on Au nanoparticles' surface in the microchannel. Dual enhancement effects are included for the as-prepared microfluidic sensor. First, acting as a gas confinement chamber, the 2D COF could effectively capture gas molecules with high adsorption capacity and fast adsorption kinetics, resulting in VOCs' preconcentration at a high level in the COF layer. At the same time, after being stacked in the microchannel, abundant hot spots were generated among the nanogaps of Au@COF NPs. The local surface plasmon resonance effect could effectively enhance the Raman intensity. Both factors contribute to the improved detection sensitivity of VOCs. As a demonstration, several representative VOCs with different functional groups were tested. The resultant Raman spectra were subjected to the statistical principal component analysis. Varied VOCs can be successfully detected with a detection limit as low as ppb level and distinguished with 95% confidence interval. The present microfluidic platform provides a simple, sensitive, and fast method for VOCs' sensing and distinguishing, which is expected to hold potential applications in the fields of health, agricultural, and environmental research.

Canine-Inspired Chemometric Analysis of Volatile Organic Compounds in Urine Headspace to Distinguish Prostate Cancer in Mice and Men

Canines can identify prostate cancer with high accuracy by smelling volatile organic compounds (VOCs) in urine. Previous studies have identified VOC biomarkers for prostate cancer utilizing solid phase microextraction (SPME) gas chromatography-mass spectrometry (GC-MS) but have not assessed the ability of VOCs to distinguish aggressive cancers. Additionally, previous investigations have utilized murine models to identify biomarkers but have not determined if the results are translatable to humans. To address these challenges, urine was collected from mice with prostate cancer and men undergoing prostate cancer biopsy and VOCs were analyzed by SPME GC-MS. Prior to analysis, SPME fibers/arrows were compared, and the fibers had enhanced sensitivity toward VOCs with a low molecular weight. The analysis of mouse urine demonstrated that VOCs could distinguish tumor-bearing mice with 100% accuracy. Linear discriminant analysis of six VOCs in human urine distinguished prostate cancer with sensitivity = 75% and specificity = 69%. Another panel of seven VOCs could classify aggressive cancer with sensitivity = 78% and specificity = 85%. These results show that VOCs have moderate accuracy in detecting prostate cancer and a superior ability to stratify aggressive tumors. Furthermore, the overlap in the structure of VOCs identified in humans and mice shows the merit of murine models for identifying biomarker candidates.

Portable Breath-Based Volatile Organic Compound Monitoring for the Detection of COVID-19 During the Circulation of the SARS-CoV-2 Delta Variant and the Transition to the SARS-CoV-2 Omicron Variant

Importance

Breath analysis has been explored as a noninvasive means to detect COVID-19. However, the impact of emerging variants of SARS-CoV-2, such as Omicron, on the exhaled breath profile and diagnostic accuracy of breath analysis is unknown.

Objective

To evaluate the diagnostic accuracies of breath analysis on detecting patients with COVID-19 when the SARS-CoV-2 Delta and Omicron variants were most prevalent.

Design, Setting, and Participants

This diagnostic study included a cohort of patients who had positive and negative test results for COVID-19 using reverse transcriptase polymerase chain reaction between April 2021 and May 2022, which covers the period when the Delta variant was overtaken by Omicron as the major variant. Patients were enrolled through intensive care units and the emergency department at the University of Michigan Health System. Patient breath was analyzed with portable gas chromatography.

Main Outcomes and Measures

Different sets of VOC biomarkers were identified that distinguished between COVID-19 (SARS-CoV-2 Delta and Omicron variants) and non-COVID-19 illness.

Results

Overall, 205 breath samples from 167 adult patients were analyzed. A total of 77 patients (mean [SD] age, 58.5 [16.1] years; 41 [53.2%] male patients; 13 [16.9%] Black and 59 [76.6%] White patients) had COVID-19, and 91 patients (mean [SD] age, 54.3 [17.1] years; 43 [47.3%] male patients; 11 [12.1%] Black and 76 [83.5%] White patients) had non-COVID-19 illness. Several patients were analyzed over multiple days. Among 94 positive samples, 41 samples were from patients in 2021 infected with the Delta or other variants, and 53 samples were from patients in 2022 infected with the Omicron variant, based on the State of Michigan and US Centers for Disease Control and Prevention surveillance data. Four VOC biomarkers were found to distinguish between COVID-19 (Delta and other 2021 variants) and non-COVID-19 illness with an accuracy of 94.7%. However, accuracy dropped substantially to 82.1% when these biomarkers were applied to the Omicron variant. Four new VOC biomarkers were found to distinguish the Omicron variant and non-COVID-19 illness (accuracy, 90.9%). Breath analysis distinguished Omicron from the earlier variants with an accuracy of 91.5% and COVID-19 (all SARS-CoV-2 variants) vs non-COVID-19 illness with 90.2% accuracy.

Conclusions and Relevance

The findings of this diagnostic study suggest that breath analysis has promise for COVID-19 detection. However, similar to rapid antigen testing, the emergence of new variants poses diagnostic challenges. The results of this study warrant additional evaluation on how to overcome these challenges to use breath analysis to improve the diagnosis and care of patients.

The Use of Biological Sensors and Instrumental Analysis to Discriminate COVID-19 Odor Signatures

The spread of SARS-CoV-2, which causes the disease COVID-19, is difficult to control as some positive individuals, capable of transmitting the disease, can be asymptomatic. Thus, it remains critical to generate noninvasive, inexpensive COVID-19 screening systems. Two such methods include detection canines and analytical instrumentation, both of which detect volatile organic compounds associated with SARS-CoV-2. In this study, the performance of trained detection dogs is compared to a noninvasive headspace-solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) approach to identifying COVID-19 positive individuals. Five dogs were trained to detect the odor signature associated with COVID-19. They varied in performance, with the two highest-performing dogs averaging 88% sensitivity and 95% specificity over five double-blind tests. The three lowest-performing dogs averaged 46% sensitivity and 87% specificity. The optimized linear discriminant analysis (LDA) model, developed using HS-SPME-GC-MS, displayed a 100% true positive rate and a 100% true negative rate using leave-one-out cross-validation. However, the non-optimized LDA model displayed difficulty in categorizing animal hair-contaminated samples, while animal hair did not impact the dogs' performance. In conclusion, the HS-SPME-GC-MS approach for noninvasive COVID-19 detection more accurately discriminated between COVID-19 positive and COVID-19 negative samples; however, dogs performed better than the computational model when non-ideal samples were presented.