Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence
Abstract Antimicrobial resistance (AMR) is a global crisis, responsible for ≈700 000 annual deaths, as reported by the World Health Organization. To counteract this growing threat to public health, innovative solutions for early detection and characterization of drug‐resistant bacterial strains are...
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2025-07-01
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| Series: | Advanced Materials Interfaces |
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| Online Access: | https://doi.org/10.1002/admi.202300664 |
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| author | Zakarya Al‐Shaebi Munevver Akdeniz Awel Olsido Ahmed Mine Altunbek Omer Aydin |
| author_facet | Zakarya Al‐Shaebi Munevver Akdeniz Awel Olsido Ahmed Mine Altunbek Omer Aydin |
| author_sort | Zakarya Al‐Shaebi |
| collection | DOAJ |
| description | Abstract Antimicrobial resistance (AMR) is a global crisis, responsible for ≈700 000 annual deaths, as reported by the World Health Organization. To counteract this growing threat to public health, innovative solutions for early detection and characterization of drug‐resistant bacterial strains are imperative. Surface‐enhanced Raman spectroscopy (SERS) combined with artificial intelligence (AI) technology presents a promising avenue to address this challenge. This review provides a concise overview of the latest advancements in SERS and AI, showcasing their transformative potential in the context of AMR. It explores the diverse methodologies proposed, highlighting their advantages and limitations. Additionally, the review underscores the significance of SERS in tandem use with machine learning (ML) and deep learning (DL) in combating AMR and emphasizes the importance of ongoing research and development efforts in this critical field. Future developments for this technology could transform the way antimicrobial resistance (AMR) is addressed and pave the way for novel approaches to the protection of public health worldwide. |
| format | Article |
| id | doaj-art-97b01fa0b78b4897b707f87e8d456a43 |
| institution | DOAJ |
| issn | 2196-7350 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Materials Interfaces |
| spelling | doaj-art-97b01fa0b78b4897b707f87e8d456a432025-08-20T02:46:27ZengWiley-VCHAdvanced Materials Interfaces2196-73502025-07-011214n/an/a10.1002/admi.202300664Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial IntelligenceZakarya Al‐Shaebi0Munevver Akdeniz1Awel Olsido Ahmed2Mine Altunbek3Omer Aydin4Department of Biomedical Engineering Erciyes University Kayseri 38039 TurkeyDepartment of Biomedical Engineering Erciyes University Kayseri 38039 TurkeyDepartment of Biomedical Engineering Erciyes University Kayseri 38039 TurkeyDepartment of Chemical Engineering University of Massachusetts Lowell Lowell MA 01854 USADepartment of Biomedical Engineering Erciyes University Kayseri 38039 TurkeyAbstract Antimicrobial resistance (AMR) is a global crisis, responsible for ≈700 000 annual deaths, as reported by the World Health Organization. To counteract this growing threat to public health, innovative solutions for early detection and characterization of drug‐resistant bacterial strains are imperative. Surface‐enhanced Raman spectroscopy (SERS) combined with artificial intelligence (AI) technology presents a promising avenue to address this challenge. This review provides a concise overview of the latest advancements in SERS and AI, showcasing their transformative potential in the context of AMR. It explores the diverse methodologies proposed, highlighting their advantages and limitations. Additionally, the review underscores the significance of SERS in tandem use with machine learning (ML) and deep learning (DL) in combating AMR and emphasizes the importance of ongoing research and development efforts in this critical field. Future developments for this technology could transform the way antimicrobial resistance (AMR) is addressed and pave the way for novel approaches to the protection of public health worldwide.https://doi.org/10.1002/admi.202300664antimicrobial resistance (AMR)artificial intelligencebacteriadeep learningmachine learningsurface‐enhanced Raman spectroscopy |
| spellingShingle | Zakarya Al‐Shaebi Munevver Akdeniz Awel Olsido Ahmed Mine Altunbek Omer Aydin Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence Advanced Materials Interfaces antimicrobial resistance (AMR) artificial intelligence bacteria deep learning machine learning surface‐enhanced Raman spectroscopy |
| title | Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence |
| title_full | Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence |
| title_fullStr | Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence |
| title_full_unstemmed | Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence |
| title_short | Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence |
| title_sort | breakthrough solution for antimicrobial resistance detection surface enhanced raman spectroscopy based on artificial intelligence |
| topic | antimicrobial resistance (AMR) artificial intelligence bacteria deep learning machine learning surface‐enhanced Raman spectroscopy |
| url | https://doi.org/10.1002/admi.202300664 |
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