Detection of SARS-CoV-2 Infection in Human Nasopharyngeal Samples by Combining MALDI-TOF MS and Artificial Intelligence

Deulofeu, Meritxell; García-Cuesta, Esteban; Peña‐Méndez, Eladia María; Conde, J. E.; Jiménez-Romero, Orlando; Verdú, Enrique; Serrando, Maria T.; Salvadó, Victòria; Boadas-Vaello, Pere

doi:10.3389/fmed.2021.661358

Cited by 28 publications

(53 citation statements)

References 41 publications

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“…Saliva spectra were classified with a high accuracy (i.e., 85%). These results are comparable or better to the performance of ML models already described for COVID-19 diagnosis [8,14]. Several studies established that the SVM model performed the best predictions [8,13,15].…”

Section: Discussionsupporting

confidence: 71%

“…We noted that the majority of studies did not evaluate performances of ML models on an independent dataset [13,15], but established the performance metrics on the training set. Two studies tested the ML models on an independent dataset [8,14]. The performance of the ML models was noticeably lower than that obtained on the training set, suggesting an overfitting of the model to the training data.…”

Section: Discussionmentioning

confidence: 99%

“…As the differences between the groups are masked by the overall heterogeneity, five ML models were implemented to specifically identify and combine protein profiles in order to increase their effectiveness in separating the groups. The training process of the models allowed us to determine that the SVM model with linear kernel was the best model, based on the F1-score [8,41]. Saliva spectra were classified with a high accuracy (i.e., 85%).…”

Section: Discussionmentioning

confidence: 99%

“…The performance of the ML models was noticeably lower than that obtained on the training set, suggesting an overfitting of the model to the training data. To avoid this problem and to obtain an unbiased measure of the performance of the model, it is essential to use an independent test set [8].…”

Section: Discussionmentioning

confidence: 99%

“…Although sensitive and specific, RT-qPCR tests remain relatively long (2-5 hours) and expensive, and the strong international demand for molecular biology consumables are factors limiting the implementation of general screening and creating tensions in reagent procurement [8].…”

Section: Introductionmentioning

confidence: 99%

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Exploratory Study on Application of MALDI-TOF-MS to Detect SARS-CoV-2 Infection in Human Saliva

Costa¹,

Martin²,

Estellon³

et al. 2021

Preprint

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SARS-CoV-2 caused a large outbreak since its emergence in December 2019. The COVID-19 diagnosis became a priority to isolate and treat infected individuals in order to break the contamination chain. Currently, the reference test for COVID-19 diagnosis is the molecular detection (RT-qPCR) of the virus from nasopharyngeal swab (NPS) samples. Although this sensitive and specific test remains the gold standard, it has several limitations, such as the invasive collection method, the relative high cost and the duration of the test. Moreover, the material shortage to perform tests due to the discrepancy between the high demand for tests and the production capacities puts additional constraints on RT-qPCR. Here, we propose a PCR-free method for diagnosing SARS-CoV-2 based on Matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS) profiling and machine learning (ML) models from salivary samples. Kinetic saliva samples were collected at enrollment and ten and thirty days later (D0, D10 and D30), to assess the classification performance of the ML models compared to the molecular tests performed on NPS specimens. Spectra were generated using an optimized protocol of saliva collection and successive quality control steps were developed to ensure the reliability of spectra. A total of 360 averaged spectra were included in the study. At D0, the comparison of MS spectra from SARS-CoV-2 positive patients (n=105) with healthy healthcare controls (n=51) revealed nine peaks that significantly distinguished the two groups. Among the five ML models tested, Support Vector Machine with Linear Kernel (SVM-LK) provided the best performance on the training dataset (accuracy = 85.2 %, sensitivity = 85.1 %, specificity = 85.3 %, F1-Score = 85.1 %). The application of the SVM-LK model on independent datasets confirmed it performances with 88.9% and 80.8% of correct classification for samples collected at D0 and D30, respectively. Conversely, at D10, the proportion of correct classification fallen to 64.3%. The analysis of saliva samples by MALDI-TOF MS and ML appears as an interesting supplementary tool for COVID-19 diagnosis, despite the mitigated results obtained for convalescent patients (D10).

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Section: Discussionsupporting

confidence: 71%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Exploratory Study on Application of MALDI-TOF-MS to Detect SARS-CoV-2 Infection in Human Saliva

Costa¹,

Martin²,

Estellon³

et al. 2021

Preprint

View full text Add to dashboard Cite

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MALDI‐TOF MS: from Microbiology to Drug Discovery

Walker

Dueñas

Ward

et al. 2023

Microbiological Identification Using MALDI‐TOF and Tandem Mass Spectrometry

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Machine learning model of imipenem‐resistant Klebsiella pneumoniae based on MALDI‐TOF‐MS platform: An observational study

Zeng

Wang

et al. 2023

Health Science Reports

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Background and AimMachine learning is an important branch and supporting technology of artificial intelligence, we established four machine learning model for the drug sensitivity of Klebsiella pneumoniae to imipenem based on matrix‐assisted laser desorption ionization time‐of‐flight mass spectrometry (MALDI‐TOF‐MS) and compared their diagnostic effect.MethodsThe data of MALDI‐TOF‐MS and imipenem sensitivity of 174 cases of K. pneumoniae isolated from clinical specimens in the laboratory of microbiology department of Tianjin Haihe Hospital from 2019 January to 2020 December were collected. The mass spectrometry and imipenem sensitivity of 70 cases of imipenem‐sensitive and 70 resistant cases were randomly selected to establish the training set model, 17 cases of sensitive and 17 cases of resistant cases were randomly selected to establish the test set model. Mass spectral peak data were subjected to orthogonal partial least squares discriminant analysis (OPLS‐DA), the training set data model was established by machine learning least absolute shrinkage and selection operator (LASSO) algorithm, logistic regression (LR) algorithm, support vector machines (SVM) algorithm, neural network (NN) algorithm, the area under the curve (AUC) and confusion matrix of training set and test set model were calculated and selected by Grid search and 3‐fold Cross‐validation respectively, the accuracy of the prediction model was verified by test set confusion matrix.ResultsThe R²Y and Q² of OPLS‐DA were 0.546 and 0.0178. The AUC of the best training set and test set models were 0.9726 and 0.9100, 1.0000 and 0.8581, 0.8462 and 0.6263, 1.0000 and 0.7180 evaluated by LASSO, LR, SVM and NN model respectively. The accuracy of the LASSO, LR, SVM and NN model were 87%, 79%, 62%, and 68% in test set, respectively.ConclusionThe LASSO prediction model of K. pneumoniae sensitivity to imipenem established in this study has a high accuracy rate and has potential clinical decision support ability.

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Detection of SARS-CoV-2 Infection in Human Nasopharyngeal Samples by Combining MALDI-TOF MS and Artificial Intelligence

Cited by 28 publications

References 41 publications

Exploratory Study on Application of MALDI-TOF-MS to Detect SARS-CoV-2 Infection in Human Saliva

Exploratory Study on Application of MALDI-TOF-MS to Detect SARS-CoV-2 Infection in Human Saliva

MALDI‐TOF MS: from Microbiology to Drug Discovery

Machine learning model of imipenem‐resistant Klebsiella pneumoniae based on MALDI‐TOF‐MS platform: An observational study

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