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Diagnostics
Volume 15
Issue 23
10.3390/diagnostics15233041
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Machine Learning Model with Fourier-Transform Infrared Spectroscopy (FTIR) as a Proof-of-Concept Tool for Predicting Group A Streptococcus (GAS) emm-Type in the Pediatric Population

by
Valeria Fox
1,†,
Gianluca Vrenna
1,†,
Martina Rossitto
1,*,
Serena Raimondi
2,
Marco Cristiano
2,
Venere Cortazzo
2,
Marilena Agosta
2,
Barbara Lucignano
2,
Manuela Onori
2,
Vanessa Tuccio Guarna Assanti
2,
Maria Stefania Lepanto
2,
Nour Essa
2,
Isabella Tarissi De Jacobis
3,
Andrea Campana
4,
Massimiliano Raponi
5,
Alberto Villani
3,
Carlo Federico Perno
2,‡ and
Paola Bernaschi
2,‡
1
Multimodal Laboratory Medicine, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy
2
Microbiology and Diagnostic Immunology Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy
3
General Pediatric and Infectious Disease Unit, Pediatric Emergency Medicine, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy
4
Pediatrics Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy
5
Medical Direction, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
‡
These authors contributed equally to this work.
Diagnostics 2025, 15(23), 3041; https://doi.org/10.3390/diagnostics15233041 (registering DOI)
Submission received: 11 September 2025 / Accepted: 26 November 2025 / Published: 28 November 2025
(This article belongs to the Special Issue Artificial Intelligence in Biomedical Diagnostics and Analysis 2025)
Versions Notes

Abstract

Background: Since 2022, invasive Group A Streptococcus (GAS) infections have increased, mainly due to the spread of specific emm-types, such as emm1. As therapy may depend on the emm-type, rapid and cost-effective identification is crucial. Fourier-transform infrared spectroscopy (FTIR) has emerged as a promising alternative to sequencing for GAS typing. We applied machine learning (ML) to FTIR spectra to build a predictive model for emm-type identification. Methods: Twenty-four GAS strains were analyzed by whole-genome sequencing and FTIR. The model was trained on twenty-one strains (emm-types: 1, 3, 4, and 6), using leave-one-out cross validation (LOOCV). To test the model’s ability to avoid false positive results, the model was also tested with three strains belonging to emm-types not included in the training of the model (emm-types: 12, 89, and 75). Results: An artificial neural network trained for 400 cycles achieved the highest accuracy (90.7%) out of the thirteen different models tested. When the three strains belonging to emm-types not included in the model were predicted with this model, it produced low score values, confirming its ability to avoid false positive results. Conclusions: We developed a preliminary and proof-of-concept model capable of accurately predicting the four most-prevalent emm-types in our setting, including the highly virulent emm1. These findings support FTIR combined with ML as a rapid, low-cost tool for GAS typing, with potential for real-time clinical applications to guide timely treatment decisions. However, as a proof-of-concept study, the relatively small sample size and limited emm-type diversity underline the need for further validation with larger and more diverse datasets. 
Keywords: FTIR; machine learning models; GAS; emm-type prediction FTIR; machine learning models; GAS; emm-type prediction

Share and Cite

MDPI and ACS Style

Fox, V.; Vrenna, G.; Rossitto, M.; Raimondi, S.; Cristiano, M.; Cortazzo, V.; Agosta, M.; Lucignano, B.; Onori, M.; Tuccio Guarna Assanti, V.; et al. Machine Learning Model with Fourier-Transform Infrared Spectroscopy (FTIR) as a Proof-of-Concept Tool for Predicting Group A Streptococcus (GAS) emm-Type in the Pediatric Population. Diagnostics 2025, 15, 3041. https://doi.org/10.3390/diagnostics15233041

AMA Style

Fox V, Vrenna G, Rossitto M, Raimondi S, Cristiano M, Cortazzo V, Agosta M, Lucignano B, Onori M, Tuccio Guarna Assanti V, et al. Machine Learning Model with Fourier-Transform Infrared Spectroscopy (FTIR) as a Proof-of-Concept Tool for Predicting Group A Streptococcus (GAS) emm-Type in the Pediatric Population. Diagnostics. 2025; 15(23):3041. https://doi.org/10.3390/diagnostics15233041

Chicago/Turabian Style

Fox, Valeria, Gianluca Vrenna, Martina Rossitto, Serena Raimondi, Marco Cristiano, Venere Cortazzo, Marilena Agosta, Barbara Lucignano, Manuela Onori, Vanessa Tuccio Guarna Assanti, and et al. 2025. "Machine Learning Model with Fourier-Transform Infrared Spectroscopy (FTIR) as a Proof-of-Concept Tool for Predicting Group A Streptococcus (GAS) emm-Type in the Pediatric Population" Diagnostics 15, no. 23: 3041. https://doi.org/10.3390/diagnostics15233041

APA Style

Fox, V., Vrenna, G., Rossitto, M., Raimondi, S., Cristiano, M., Cortazzo, V., Agosta, M., Lucignano, B., Onori, M., Tuccio Guarna Assanti, V., Lepanto, M. S., Essa, N., Tarissi De Jacobis, I., Campana, A., Raponi, M., Villani, A., Perno, C. F., & Bernaschi, P. (2025). Machine Learning Model with Fourier-Transform Infrared Spectroscopy (FTIR) as a Proof-of-Concept Tool for Predicting Group A Streptococcus (GAS) emm-Type in the Pediatric Population. Diagnostics, 15(23), 3041. https://doi.org/10.3390/diagnostics15233041

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