Comparative performance of two automated machine learning platforms for COVID-19 detection by MALDI-TOF-MS

• Published in: PloS one Vol. 17; no. 7; p. e0263954
• Main Authors: Rashidi, Hooman H., Pepper, John, Howard, Taylor, Klein, Karina, May, Larissa, Albahra, Samer, Phinney, Brett, Salemi, Michelle R., Tran, Nam K.
• Format: Journal Article
• Language: English
• Published: San Francisco Public Library of Science 29.07.2022; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Automation; Biology and life sciences; Computer and Information Sciences; Coronaviruses; COVID-19; COVID-19 vaccines; Datasets; Evaluation; Infectious diseases; Innovations; Learning algorithms; Machine learning; Mass spectrometry; Mass spectroscopy; Medical tests; Medicine and Health Sciences; Pandemics; Physical Sciences; Platforms; Public health; Research and Analysis Methods; Severe acute respiratory syndrome; Severe acute respiratory syndrome coronavirus 2; Spectroscopy; Viral diseases; United States
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0263954

The 2019 novel coronavirus infectious disease (COVID-19) pandemic has resulted in an unsustainable need for diagnostic tests. Currently, molecular tests are the accepted standard for the detection of SARS-CoV-2. Mass spectrometry (MS) enhanced by machine learning (ML) has recently been postulated to serve as a rapid, high-throughput, and low-cost alternative to molecular methods. Automated ML is a novel approach that could move mass spectrometry techniques beyond the confines of traditional laboratory settings. However, it remains unknown how different automated ML platforms perform for COVID-19 MS analysis. To this end, the goal of our study is to compare algorithms produced by two commercial automated ML platforms (Platforms A and B). Our study consisted of MS data derived from 361 subjects with molecular confirmation of COVID-19 status including SARS-CoV-2 variants. The top optimized ML model with respect to positive percent agreement (PPA) within Platforms A and B exhibited an accuracy of 94.9%, PPA of 100%, negative percent agreement (NPA) of 93%, and an accuracy of 91.8%, PPA of 100%, and NPA of 89%, respectively. These results illustrate the MS method’s robustness against SARS-CoV-2 variants and highlight similarities and differences in automated ML platforms in producing optimal predictive algorithms for a given dataset.