Application of MALDI-MS and Machine Learning to Detection of SARS-CoV-2 and non-SARS-CoV-2 Respiratory Infections
Bibliographic record
Abstract
Abstract Background Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) could aid the diagnosis of acute respiratory infections (ARI) owing to its affordability and high-throughput capacity. MALDI-MS has been proposed for use on commonly available respiratory samples, without specialized sample preparation, making this technology especially attractive for implementation in low-resource regions. Here, we assessed the utility of MALDI-MS in differentiating SARS-CoV-2 versus non-COVID acute respiratory infections (NCARI) in a clinical lab setting of Kazakhstan. Methods Nasopharyngeal swabs were collected from in- and outpatients with respiratory symptoms and from asymptomatic controls (AC) in 2020-2022. PCR was used to differentiate SARS-CoV-2+ and NCARI cases. MALDI-MS spectra were obtained for a total of 252 samples (115 SARS-CoV-2+, 98 NCARI and 39 AC) without specialized sample preparation. In our first sub-analysis, we followed a published protocol for peak preprocessing and Machine Learning (ML), trained on publicly available spectra from South American SARS-CoV-2+ and NCARI samples. In our second sub-analysis, we trained ML models on a peak intensity matrix representative of both South American (SA) and Kazakhstan (Kaz) samples. Results Applying the established MALDI-MS pipeline ”as is” resulted in a high detection rate for SARS-CoV-2+ samples (91.0%), but low accuracy for NCARI (48.0%) and AC (67.0%) by the top-performing random forest model. After re-training of the ML algorithms on the SA-Kaz peak intensity matrix, the accuracy of detection by the top-performing Support Vector Machine with radial basis function kernel model was at 88.0, 95.0 and 78% for the Kazakhstan SARS-CoV-2+, NCARI, and AC subjects, respectively with a SARS-CoV-2 vs. rest ROC AUC of 0.983 [0.958, 0.987]; a high differentiation accuracy was maintained for the South American SARS-CoV-2 and NCARI. Conclusions MALDI-MS/ML is a feasible approach for the differentiation of ARI without a specialized sample preparation. The implementation of MALDI-MS/ML in a real clinical lab setting will necessitate continuous optimization to keep up with the rapidly evolving landscape of ARI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".