Deep Learning for Rapid Detection of Bacterial Infections from Microscopic and Clinical Data: AI-Based Identification of Staphylococcus aureus
محورهای موضوعی : Biotechnological Journal of Environmental MicroorganismsFatemeh Mousalou 1 , Seyedeh Negin Nedaei 2
1 - Department of Microbiology, Ard.C., Islamic Azad University, Ardabil, Iran
2 - Department of Microbiology, Ard.C., Islamic Azad University, Ardabil, Iran
کلید واژه: Staphylococcus aureus, MRSA, bloodstream infection, artificial intelligence, bacterial infection, deep learning.,
چکیده مقاله :
Infections of the bloodstream, pneumonia, and bone and joint infections are all well-known symptoms of Staphylococcus aureus (S. aureus), which are frequently fatal. MRSA, a potentially harmful strain that is resistant to methicillin, an antibiotic produced from penicillin, is associated with AMR. It is still quite challenging to diagnose and treat bacterial infections in the medical industry in the modern world. Artificial Intelligence (AI) has emerged as a potent new method for detecting and treating bacterial infections. Since timely diagnosis and treatment can improve morbidity and mortality for bacterial diseases and other infectious diseases, such hepatocellular carcinoma brought about by hepatitis B and C, or non-infectious disorders such acute necrotizing pancreatitis. Because of its prevalence and considerable clinical load in hospitals, methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infection (BSI) is a major worry. Due to its resistance to several antibiotics and associated complications like septic shock and metastatic infections, it poses a serious clinical challenge. For categorizing bacterial antibiotic resistance and susceptibility, deep learning is preferred over traditional machine learning due to its better performance. The macroscopic level at which traditional techniques, like the Kirby-Bauer disk-diffusion test, are performed, restricts accuracy and ignores important microscopic bacterial interactions. There are still many obstacles to overcome despite the tremendous promise of AI to revolutionize tailored treatment. Ultimately, as AI technology develops and is used more extensively, it will help doctors to treat bacterial infections more effectively, advancing the medical industry toward more accurate, efficient, and individualized treatment.
Infections of the bloodstream, pneumonia, and bone and joint infections are all well-known symptoms of Staphylococcus aureus (S. aureus), which are frequently fatal. MRSA, a potentially harmful strain that is resistant to methicillin, an antibiotic produced from penicillin, is associated with AMR. It is still quite challenging to diagnose and treat bacterial infections in the medical industry in the modern world. Artificial Intelligence (AI) has emerged as a potent new method for detecting and treating bacterial infections. Since timely diagnosis and treatment can improve morbidity and mortality for bacterial diseases and other infectious diseases, such hepatocellular carcinoma brought about by hepatitis B and C, or non-infectious disorders such acute necrotizing pancreatitis. Because of its prevalence and considerable clinical load in hospitals, methicillin-resistant Staphylococcus aureus (MRSA) bloodstream infection (BSI) is a major worry. Due to its resistance to several antibiotics and associated complications like septic shock and metastatic infections, it poses a serious clinical challenge. For categorizing bacterial antibiotic resistance and susceptibility, deep learning is preferred over traditional machine learning due to its better performance. The macroscopic level at which traditional techniques, like the Kirby-Bauer disk-diffusion test, are performed, restricts accuracy and ignores important microscopic bacterial interactions. There are still many obstacles to overcome despite the tremendous promise of AI to revolutionize tailored treatment. Ultimately, as AI technology develops and is used more extensively, it will help doctors to treat bacterial infections more effectively, advancing the medical industry toward more accurate, efficient, and individualized treatment.
1. Al‑Shaebi, Z., Çiloğlu, Ü., Nasser, M., Kahraman, M., & Aydın, Ö. (2024). Staphylococcus Aureus‑Related antibiotic resistance detection using synergy of Surface‑Enhanced Raman spectroscopy and deep learning. Biomedical Signal Processing and Control, 91, Article 105933.
2. Kang, M. W., & Ahn, S. Y. (2025). Optimising personalised antibiotic treatment for methicillin‑resistant Staphylococcus aureus bloodstream infections in ICU patients using a deep learning‑based causal inference approach. Journal of Global Antimicrobial Resistance, 45, 70‑76.
3. Nayak, D. S. K., Dash, A., Routray, S. P., Sahoo, S., & Swarnkar, T. (2024, September). ARGai: AI Fusion Approaches for Classification of S. aureus Strains and Identification of Antibiotic Resistant Genes. In 2024 International Conference on Artificial Intelligence and Emerging Technology (Global AI Summit) (pp. 1236-1241). IEEE.
4. Parra-Sánchez, L. F., Rivera-Manrique, S. I., González-Vega, A., & Padierna, L. C. (2025). A novel antibacterial susceptibility test from microscopic images by deep learning. IEEE Access.
5. Rahman, M. H., Aunni, S. A. A., Ahmed, B., Rahman, M. M., Shabuj, M. M. H., Das, D. C., Akter, M. S., Numan, A. A. (2024). "Artificial intelligence for Improved Diagnosis and Treatment of Bacterial Infections", Microbial Bioactives, 7(1),1-18,10036
6. Zhang, X., Zhang, D., Zhang, X., & Zhang, X. (2024). Artificial intelligence applications in the diagnosis and treatment of bacterial infections. Frontiers in Microbiology, 15, 1449844.
