A New and Fast Approach for Antimicrobial Resistance Detection: Combination of Artificial Intelligence and Surface-Enhanced Raman Spectra

AYDIN Ö., Al-Shaebi Z., Akdeniz M., Kursunluoglu G., Zarasız G., YERLİTAŞ S. İ., ...Daha Fazla

16th Mediterranean Conference on Medical and Biological Engineering and Computing, MEDICON 2023 and 5th International Conference on Medical and Biological Engineering, CMBEBIH 2023, Sarajevo, Bosna-Hersek, 14 - 16 Eylül 2023, cilt.94, ss.98-103 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 94
  • Doi Numarası: 10.1007/978-3-031-49068-2_11
  • Basıldığı Şehir: Sarajevo
  • Basıldığı Ülke: Bosna-Hersek
  • Sayfa Sayıları: ss.98-103
  • Anahtar Kelimeler: Antimicrobial resistance, Machine learnings, Methicillin-resistant S. aureus (MRSA), Surface-enhanced raman spectroscopy
  • Erciyes Üniversitesi Adresli: Evet

Özet

Antimicrobial resistance (AMR) in bacteria is a global health crisis due to the rapid emergence of multidrug-resistant bacteria and the lengthy development of new antimicrobials. Surface-enhanced Raman scattering (SERS) is a powerful technique for sensitive label-free analysis of chemical and biological samples, which can be used for rapid detection and identification of bacterial strains. However, distinguishing the antibiotic-resistant and susceptible bacteria by SERS spectra is challenging due to the high molecular similarity of the bacterial strains. To overcome this challenge, we proposed to use artificial intelligence (AI) methods to assist SERS-based diagnostics of AMR bacteria. We used machine learning to optimize the sampling of SERS substrates, improving the data collection efficiency and reliability. We also used deep learning to analyze the SERS spectra of bacteria. Our AI-assisted SERS strategy enables label-free spectroscopic profiling of AMR bacteria in complex clinical settings, offering a promising solution for combating the AMR threat.