Identification of methicillin-resistant <i>Staphylococcus aureus</i> bacteria using surface-enhanced Raman spectroscopy and machine learning techniques

Ciloglu, Fatma Uysal; Saridag, Ayse Mine; Kılıç, İbrahim Halil; Tokmakçı, Mahmut; Kahraman, Mehmet; Aydın, Ömer

doi:10.1039/d0an00476f

Cited by 86 publications

(76 citation statements)

References 54 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although the typical procedure normally takes 3–4 days or even longer for fastidious bacteria on average to obtain the final AST results ( Han et al, 2020 ), with MALDI-TOF MS-based approaches, i.e., for positive blood culture bottles, a result can be obtained after <24 h, in some cases also the same day ( Verroken et al, 2014 ). Due to its simple operations, RS, especially SERS, has been used for testing antibiotic resistance phenotypes in many bacterial species, such as E. coli ( Chang et al, 2019 ), S. aureus ( Uysal Ciloglu et al, 2020 ), and Pseudomonas aeruginosa ( Li et al, 2019 ). A variety of signatures have been observed in terms of bacterial antibiotic resistance and susceptibility, which could be used for rapidly identifying resistance to sublethal concentrations of antibiotics ( Galvan and Yu, 2018 ; Han et al, 2020 ).…”

Section: Antibiotic Resistance Profilingmentioning

confidence: 99%

“…Due to the complexity of Raman spectra, statistics and machine learning algorithms, rather than traditional linear analysis, are normally involved in data processing procedures. So far, many machine learning methods have been introduced into Raman spectra analysis, such as artificial neural network, deep learning, and Monte Carlo estimation ( Lu et al, 2012 ; Moawad et al, 2019 ; Lussier et al, 2020 ; Uysal Ciloglu et al, 2020 ). In the rapid characterization of Staphylococcus , Rebrošová et al (2017) compared three machine learning methods, namely, linear discriminant analysis, one nearest neighbor, and support vector machine (SVM), all of which showed efficient identification of staphylococci using RS with high accuracy.…”

Section: Computational Analysis Of Raman Spectramentioning

confidence: 99%

See 1 more Smart Citation

Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings

et al. 2021

View full text Add to dashboard Cite

Infectious diseases caused by bacterial pathogens are important public issues. In addition, due to the overuse of antibiotics, many multidrug-resistant bacterial pathogens have been widely encountered in clinical settings. Thus, the fast identification of bacteria pathogens and profiling of antibiotic resistance could greatly facilitate the precise treatment strategy of infectious diseases. So far, many conventional and molecular methods, both manual or automatized, have been developed for in vitro diagnostics, which have been proven to be accurate, reliable, and time efficient. Although Raman spectroscopy (RS) is an established technique in various fields such as geochemistry and material science, it is still considered as an emerging tool in research and diagnosis of infectious diseases. Based on current studies, it is too early to claim that RS may provide practical guidelines for microbiologists and clinicians because there is still a gap between basic research and clinical implementation. However, due to the promising prospects of label-free detection and noninvasive identification of bacterial infections and antibiotic resistance in several single steps, it is necessary to have an overview of the technique in terms of its strong points and shortcomings. Thus, in this review, we went through recent studies of RS in the field of infectious diseases, highlighting the application potentials of the technique and also current challenges that prevent its real-world applications.

show abstract

Section: Antibiotic Resistance Profilingmentioning

confidence: 99%

Section: Computational Analysis Of Raman Spectramentioning

confidence: 99%

Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The antibiotic resistance of MRSA and MSSA was confirmed the presence or absence of mec A gene using the Polymerase Chain Reaction (PCR) technique. Furthermore, the disc diffusion method was also used for the antibiotic resistance confirmation and details can be found in our previous study 15 . According to PCR and disc diffusion method results methicillin resistance was found in all MRSA bacteria.…”

Section: Methodsmentioning

confidence: 99%

“…Therefore, the SERS technique requires advanced data processing algorithms to capture these minor differences. A vast majority of publications have reported that machine learning techniques can be employed to discriminate antibioticresistant and susceptible bacteria by using data obtained from SERS [15][16][17][18] .…”

Section: Introductionmentioning

confidence: 99%

“…Although raw data were used in our study, high classification accuracy and AUC were found thanks to the SERS technique with high SNR using SAE-based DNN which successfully extracts features from the data. Our group previously has applied traditional classifiers such as KNN, SVM, DT and naïve Bayes (NB) for the discrimination of MRSA, MSSA, and Legionella pneumophila bacteria15 . KNN classifier has provided best accuracy with 97.8% among other techniques.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Drug-resistant Staphylococcus Aureus Bacteria Detection with the Combination of Surface-enhanced Raman Spectroscopy and Deep Learning Techniques

Ciloglu

Çalışkan

Saridag

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door - antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with other traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared with other traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data. The proposed method is a label-free, easy implemented, and reliable technique with high sensitivity for clinical use.

show abstract

Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence

Al‐Shaebi,

Akdeniz,

Ahmed

et al. 2023

Adv Materials Inter

View full text Add to dashboard Cite

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.

show abstract

Identification of methicillin-resistant Staphylococcus aureus bacteria using surface-enhanced Raman spectroscopy and machine learning techniques

Abstract: To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes that are...

Cited by 86 publications

References 54 publications

Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings

Applications of Raman Spectroscopy in Bacterial Infections: Principles, Advantages, and Shortcomings

Drug-resistant Staphylococcus Aureus Bacteria Detection with the Combination of Surface-enhanced Raman Spectroscopy and Deep Learning Techniques

Breakthrough Solution for Antimicrobial Resistance Detection: Surface‐Enhanced Raman Spectroscopy‐based on Artificial Intelligence

Contact Info

Product

Resources

About