Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learning
Background: Rapid and accurate identification of bacteria is required in order to develop effective treatment strategies. Traditional culture-based methods are time-consuming, while MALDI-TOF MS is expensive. The Raman spectroscopy, due to its relatively cost-effectiveness, offers a promising altern...
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Elsevier
2025-02-01
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| Series: | Journal of Microbiology, Immunology and Infection |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1684118224002202 |
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| author | Yu-Tzu Lin Hsiu-Hsien Lin Chih-Hao Chen Kun-Hao Tseng Pang-Chien Hsu Ya-Lun Wu Wei-Cheng Chang Nai-Shun Liao Yi-Fan Chou Chun-Yi Hsu Yu-Hui Liao Mao-Wang Ho Shih-Sheng Chang Po-Ren Hsueh Der-Yang Cho |
| author_facet | Yu-Tzu Lin Hsiu-Hsien Lin Chih-Hao Chen Kun-Hao Tseng Pang-Chien Hsu Ya-Lun Wu Wei-Cheng Chang Nai-Shun Liao Yi-Fan Chou Chun-Yi Hsu Yu-Hui Liao Mao-Wang Ho Shih-Sheng Chang Po-Ren Hsueh Der-Yang Cho |
| author_sort | Yu-Tzu Lin |
| collection | DOAJ |
| description | Background: Rapid and accurate identification of bacteria is required in order to develop effective treatment strategies. Traditional culture-based methods are time-consuming, while MALDI-TOF MS is expensive. The Raman spectroscopy, due to its relatively cost-effectiveness, offers a promising alternative for bacterial identification. However, its clinical utility still requires further validation. Methods: In this study, the artificial intelligent Raman detection and identification system (AIRDIS) was implemented to identify bacterial species, including Staphylococcus aureus (n = 1290), Enterococcus faecium (n = 1020), Klebsiella pneumoniae (n = 1366), Pseudomonas aeruginosa (n = 1067), and Acinetobacter baumannii (n = 811). Raman spectra were collected, preprocessed, and analyzed by machine learning (ML). Results: After training on 24,420 Raman spectra from 1221 isolates and testing on 4333 isolates, the AIRDIS demonstrated an area under the curve (AUC) of 0.99 for Gram classification, with accuracies of 97.64 % for Gram-positive bacteria and 98.86 % for Gram-negative bacteria. Spectral differences between Gram-positive and Gram-negative bacteria were linked to structural variations in their cell walls, such as peptidoglycan and lipopolysaccharides. At the species level, S. aureus, E. faecium, K. pneumoniae, P. aeruginosa, and A. baumannii were identified with high accuracy, ranging from 94.76 % to 96.88 %, with all species achieving an AUC of 0.99. Conclusions: Validation with a large number of clinical isolates demonstrated Raman spectroscopy combined with ML excels in identification of five bacterial species associated with multidrug resistance. This finding confirms the clinical utility of the system while laying a solid foundation for the future development of antimicrobial resistance prediction models. |
| format | Article |
| id | doaj-art-df7cdb2fd76f41afb68ca3444c7547fa |
| institution | Kabale University |
| issn | 1684-1182 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Microbiology, Immunology and Infection |
| spelling | doaj-art-df7cdb2fd76f41afb68ca3444c7547fa2025-02-06T05:11:21ZengElsevierJournal of Microbiology, Immunology and Infection1684-11822025-02-015817785Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learningYu-Tzu Lin0Hsiu-Hsien Lin1Chih-Hao Chen2Kun-Hao Tseng3Pang-Chien Hsu4Ya-Lun Wu5Wei-Cheng Chang6Nai-Shun Liao7Yi-Fan Chou8Chun-Yi Hsu9Yu-Hui Liao10Mao-Wang Ho11Shih-Sheng Chang12Po-Ren Hsueh13Der-Yang Cho14Department of Medical Laboratory Science and Biotechnology, China Medical University, Taichung, TaiwanDepartment of Laboratory Medicine, China Medical University Hospital, China Medical University, Taichung, TaiwanDivision of Infectious Diseases, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, TaiwanDepartment of Laboratory Medicine, China Medical University Hospital, China Medical University, Taichung, TaiwanDepartment of Laboratory Medicine, China Medical University Hospital, China Medical University, Taichung, TaiwanAI Innovation Center, China Medical University Hospital, Taichung City, TaiwanITRUST MedTech Inc., Hsinchu, TaiwanITRUST MedTech Inc., Hsinchu, TaiwanITRUST MedTech Inc., Hsinchu, TaiwanITRUST MedTech Inc., Hsinchu, TaiwanITRUST MedTech Inc., Hsinchu, TaiwanDivision of Infectious Diseases, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, TaiwanAI Innovation Center, China Medical University Hospital, Taichung City, TaiwanDepartment of Laboratory Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan; Division of Infectious Diseases, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan; Corresponding author. Departments of Laboratory Medicine and Internal Medicine, China Medical University Hospital, School of Medicine, China Medical University, Taichung, Taiwan.Department of Neurosurgery, China Medical University Hospital, Taichung, TaiwanBackground: Rapid and accurate identification of bacteria is required in order to develop effective treatment strategies. Traditional culture-based methods are time-consuming, while MALDI-TOF MS is expensive. The Raman spectroscopy, due to its relatively cost-effectiveness, offers a promising alternative for bacterial identification. However, its clinical utility still requires further validation. Methods: In this study, the artificial intelligent Raman detection and identification system (AIRDIS) was implemented to identify bacterial species, including Staphylococcus aureus (n = 1290), Enterococcus faecium (n = 1020), Klebsiella pneumoniae (n = 1366), Pseudomonas aeruginosa (n = 1067), and Acinetobacter baumannii (n = 811). Raman spectra were collected, preprocessed, and analyzed by machine learning (ML). Results: After training on 24,420 Raman spectra from 1221 isolates and testing on 4333 isolates, the AIRDIS demonstrated an area under the curve (AUC) of 0.99 for Gram classification, with accuracies of 97.64 % for Gram-positive bacteria and 98.86 % for Gram-negative bacteria. Spectral differences between Gram-positive and Gram-negative bacteria were linked to structural variations in their cell walls, such as peptidoglycan and lipopolysaccharides. At the species level, S. aureus, E. faecium, K. pneumoniae, P. aeruginosa, and A. baumannii were identified with high accuracy, ranging from 94.76 % to 96.88 %, with all species achieving an AUC of 0.99. Conclusions: Validation with a large number of clinical isolates demonstrated Raman spectroscopy combined with ML excels in identification of five bacterial species associated with multidrug resistance. This finding confirms the clinical utility of the system while laying a solid foundation for the future development of antimicrobial resistance prediction models.http://www.sciencedirect.com/science/article/pii/S1684118224002202Raman spectroscopyMALDI-TOF MSArtificial intelligenceMachine learningSpecies identification |
| spellingShingle | Yu-Tzu Lin Hsiu-Hsien Lin Chih-Hao Chen Kun-Hao Tseng Pang-Chien Hsu Ya-Lun Wu Wei-Cheng Chang Nai-Shun Liao Yi-Fan Chou Chun-Yi Hsu Yu-Hui Liao Mao-Wang Ho Shih-Sheng Chang Po-Ren Hsueh Der-Yang Cho Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learning Journal of Microbiology, Immunology and Infection Raman spectroscopy MALDI-TOF MS Artificial intelligence Machine learning Species identification |
| title | Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learning |
| title_full | Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learning |
| title_fullStr | Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learning |
| title_full_unstemmed | Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learning |
| title_short | Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligent Raman Detection and Identification System (AIRDIS) with machine learning |
| title_sort | identification of staphylococcus aureus enterococcus faecium klebsiella pneumoniae pseudomonas aeruginosa and acinetobacter baumannii from raman spectra by artificial intelligent raman detection and identification system airdis with machine learning |
| topic | Raman spectroscopy MALDI-TOF MS Artificial intelligence Machine learning Species identification |
| url | http://www.sciencedirect.com/science/article/pii/S1684118224002202 |
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