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Artificial Intelligence and Microbiology

Yıl 2024, Cilt: 5 Sayı: 2, 119 - 128
https://doi.org/10.46871/eams.1458704

Öz

The concept of Artificial Intelligence (AI) is increasingly important in the healthcare sector today. Components of AI such as machine learning and deep learning are being utilized in various applications within the field of microbiology. This study examines the uses of AI in microbiology and its role in healthcare applications.
Machine learning enables computer systems to analyze data using algorithms that mimic human intelligence, while deep learning processes information through multi-layered artificial neural networks. These technologies are used in many areas such as microbiological diagnosis, drug discovery, infection control, and patient monitoring.
For instance, AI-supported systems are used in microbiological diagnosis to shorten diagnosis times and increase accuracy compared to traditional methods. Additionally, smart systems developed for preventing hospital-acquired infections alert hospital staff, thus reducing the risk of infection.
AI also plays a significant role in the diagnosis of microorganisms such as viruses and fungi. Especially, AI-supported image analysis methods are utilized for rapid and accurate diagnosis. However, there are some challenges in the use of AI. Issues related to data privacy and ethics are among the factors limiting the applications of AI in microbiology and healthcare. Furthermore, the cost and complexity of algorithm implementation pose additional challenges.
By discussing the applications of AI in microbiology and its potential in the future, this study sheds light on innovative developments in the healthcare sector.

Kaynakça

  • 1. Ergüven Ö, Ökten S. Yapay Zeka'nın Mikrobiyolojide Kullanımı. Journal of Artificial Intelligence in Health Sciences. 2022;2(2):1-12.
  • 2. https://www.oracle.com/tr/artificial-intelligence/machine-learning/.(Last access date: 15.03.2024)
  • 3. Stephens K. Radiology Partners, Aidoc Partner to Accelerate the Use of Artificial Intelligence. , 2021, AXIS Imaging News..
  • 4. Wee IJY, Kuo LJ, Ngu JC. A systematic review of the true benefit of robotic surgery: Ergonomics. International Journal of Medical Robotics. 2020;16(4):e2113.
  • 5. Paul D, Sanap G, Shenoy S, et al. Artificial intelligence in drug discovery and development. Drug Discovery Today. 2021;26(1):80-93.
  • 6. Agrebi S, Larbi A. Use of artificial intelligence in infectious diseases. Artificial Intelligence in Precision Health, 2020. p. 415-38.
  • 7. Tran NK, Albahra S, May L, et al. Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing. Clinical Chemistry. 2021;68(1):125-33.
  • 8. Smith KP, Wang H, Durant TJS, et al. Applications of Artificial Intelligence in Clinical Microbiology Diagnostic Testing. Clinical Microbiology Newsletter. 2020;42(8):61-70.
  • 9. Zielinski B, Plichta A, Misztal K, et al. Deep learning approach to bacterial colony classification. PLoS One. 2017;12(9):e0184554.
  • 10. Huang T, Ma Y, Li S, et al. Effectiveness of an artificial intelligence-based training and monitoring system in prevention of nosocomial infections: A pilot study of hospital-based data. Drug Discovery & Therapeutics. 2023;17(5):351-6.
  • 11. Wieser A, Schneider L, Jung J, et al. MALDI-TOF MS in microbiological diagnostics-identification of microorganisms and beyond (mini review). Applied Microbiology and Biotechnology. 2012;93(3):965-74.
  • 12. Wang HY, Chen CH, Lee TY, et al. Rapid Detection of Heterogeneous Vancomycin-Intermediate Staphylococcus aureus Based on Matrix-Assisted Laser Desorption Ionization Time-of-Flight: Using a Machine Learning Approach and Unbiased Validation. Frontiers in Microbiology. 2018;9:2393.
  • 13. Glasson J, Hill R, Summerford M, et al. Multicenter Evaluation of an Image Analysis Device (APAS): Comparison Between Digital Image and Traditional Plate Reading Using Urine Cultures. Annals of Laboratory Medicine. 2017;37(6):499-504.
  • 14. Ma L, Yi J, Wisuthiphaet N, et al. Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging. Applied and Environmental Microbiology. 2023;89(1):e01828-22.
  • 15. Du J, Su Y, Qiao J, et al. Application of artificial intelligence in diagnosis of pulmonary tuberculosis. Chinese Medical Journal (English). 2024;137(5):559-61.
  • 16. https://www.who.int/data/stories/the-true-death-toll-of-covid-19-estimating-global-excess-mortality. (Last access date 15.03.2024)
  • 17. Widodo S, Tumarta Arif YW. Early Detect of Covid-19 from Clinical Symptoms Based on Artificial Intelligence. International Journal of Advanced Engineering and Management Research. 2024;09(01):86-98.
  • 18. Sitaula C, Shahi TB. Monkeypox virus detection using pre-trained deep learning-based approaches. Journal of Medical Systems. 2022;46(11):78.
  • 19. Zielinski B, Sroka-Oleksiak A, Rymarczyk D, et al. Deep learning approach to describe and classify fungi microscopic images. PLoS One. 2020;15(6):e0234806.
  • 20. Singla N, Kundu R, Dey P. Artificial Intelligence: Exploring utility in detection and typing of fungus with futuristic application in fungal cytology. Cytopathology. 2024;35(2):226-34.
  • 21. Ma H, Yang J, Chen X, et al. Deep convolutional neural network: a novel approach for the detection of Aspergillus fungi via stereomicroscopy. Journal of Microbiology. 2021;59(6):563-72.
  • 22. Liu R, Liu T, Dan T, et al. AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images. Patterns (N Y). 2023;4(9):100806.
  • 23. Fisch D, Evans R, Clough B, et al. HRMAn 2.0: Next-generation artificial intelligence-driven analysis for broad host-pathogen interactions. Cellular Microbiology. 2021;23(7):e13349.
  • 24. Diab R. Artificial intelligence and Medical Parasitology: Applications and perspectives. Parasitologists United Journal. 2023;16(2):91-3.

Yapay Zeka ve Mikrobiyoloji

Yıl 2024, Cilt: 5 Sayı: 2, 119 - 128
https://doi.org/10.46871/eams.1458704

Öz

Yapay Zekâ (YZ) kavramı, günümüzde sağlık sektöründe giderek artan bir öneme sahiptir. Makine öğrenmesi ve derin öğrenme gibi YZ bileşenleri, mikrobiyoloji alanında çeşitli uygulamalarda kullanılmaktadır. Bu çalışma, YZ'nin mikrobiyoloji alanındaki kullanımlarını ve sağlık uygulamalarındaki rolünü incelemektedir.
Makine öğrenmesi, bilgisayar sistemlerinin insan zekasını taklit eden algoritmalarla veri analizi yapmasını sağlar. Derin öğrenme ise çok katmanlı yapay sinir ağları aracılığıyla bilgiyi işler. Bu teknolojiler, mikrobiyolojik tanı, ilaç keşfi, enfeksiyon kontrolü ve hasta izleme gibi pek çok alanda kullanılmaktadır.
Örneğin, mikrobiyolojik tanıda geleneksel yöntemler yerine YZ destekli sistemler kullanılarak tanı süreleri kısaltılmakta ve doğruluk oranları artırılmaktadır. Ayrıca, hastane enfeksiyonlarının önlenmesi için geliştirilen akıllı sistemler, hastane personelini uyararak enfeksiyon riskini azaltmaktadır.
YZ'nin virüsler ve mantarlar gibi mikroorganizmaların tanısında da önemli bir rolü vardır. Özellikle, yapay zekâ destekli görüntü analizi yöntemleri, hızlı ve doğru tanı koymada kullanılmaktadır. Ancak, YZ'nin kullanımında bazı zorluklar da bulunmaktadır. Veri gizliliği ve etik konular, YZ'nin mikrobiyoloji ve sağlık alanındaki uygulamalarını sınırlayan faktörler arasındadır. Ayrıca, algoritmaların maliyeti ve kullanımının karmaşıklığı da bu zorluklar arasındadır.
Bu çalışma, YZ'nin mikrobiyoloji alanındaki uygulamalarını ve gelecekteki potansiyelini tartışarak, sağlık sektöründeki yenilikçi gelişmelere ışık tutmaktadır.

Kaynakça

  • 1. Ergüven Ö, Ökten S. Yapay Zeka'nın Mikrobiyolojide Kullanımı. Journal of Artificial Intelligence in Health Sciences. 2022;2(2):1-12.
  • 2. https://www.oracle.com/tr/artificial-intelligence/machine-learning/.(Last access date: 15.03.2024)
  • 3. Stephens K. Radiology Partners, Aidoc Partner to Accelerate the Use of Artificial Intelligence. , 2021, AXIS Imaging News..
  • 4. Wee IJY, Kuo LJ, Ngu JC. A systematic review of the true benefit of robotic surgery: Ergonomics. International Journal of Medical Robotics. 2020;16(4):e2113.
  • 5. Paul D, Sanap G, Shenoy S, et al. Artificial intelligence in drug discovery and development. Drug Discovery Today. 2021;26(1):80-93.
  • 6. Agrebi S, Larbi A. Use of artificial intelligence in infectious diseases. Artificial Intelligence in Precision Health, 2020. p. 415-38.
  • 7. Tran NK, Albahra S, May L, et al. Evolving Applications of Artificial Intelligence and Machine Learning in Infectious Diseases Testing. Clinical Chemistry. 2021;68(1):125-33.
  • 8. Smith KP, Wang H, Durant TJS, et al. Applications of Artificial Intelligence in Clinical Microbiology Diagnostic Testing. Clinical Microbiology Newsletter. 2020;42(8):61-70.
  • 9. Zielinski B, Plichta A, Misztal K, et al. Deep learning approach to bacterial colony classification. PLoS One. 2017;12(9):e0184554.
  • 10. Huang T, Ma Y, Li S, et al. Effectiveness of an artificial intelligence-based training and monitoring system in prevention of nosocomial infections: A pilot study of hospital-based data. Drug Discovery & Therapeutics. 2023;17(5):351-6.
  • 11. Wieser A, Schneider L, Jung J, et al. MALDI-TOF MS in microbiological diagnostics-identification of microorganisms and beyond (mini review). Applied Microbiology and Biotechnology. 2012;93(3):965-74.
  • 12. Wang HY, Chen CH, Lee TY, et al. Rapid Detection of Heterogeneous Vancomycin-Intermediate Staphylococcus aureus Based on Matrix-Assisted Laser Desorption Ionization Time-of-Flight: Using a Machine Learning Approach and Unbiased Validation. Frontiers in Microbiology. 2018;9:2393.
  • 13. Glasson J, Hill R, Summerford M, et al. Multicenter Evaluation of an Image Analysis Device (APAS): Comparison Between Digital Image and Traditional Plate Reading Using Urine Cultures. Annals of Laboratory Medicine. 2017;37(6):499-504.
  • 14. Ma L, Yi J, Wisuthiphaet N, et al. Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging. Applied and Environmental Microbiology. 2023;89(1):e01828-22.
  • 15. Du J, Su Y, Qiao J, et al. Application of artificial intelligence in diagnosis of pulmonary tuberculosis. Chinese Medical Journal (English). 2024;137(5):559-61.
  • 16. https://www.who.int/data/stories/the-true-death-toll-of-covid-19-estimating-global-excess-mortality. (Last access date 15.03.2024)
  • 17. Widodo S, Tumarta Arif YW. Early Detect of Covid-19 from Clinical Symptoms Based on Artificial Intelligence. International Journal of Advanced Engineering and Management Research. 2024;09(01):86-98.
  • 18. Sitaula C, Shahi TB. Monkeypox virus detection using pre-trained deep learning-based approaches. Journal of Medical Systems. 2022;46(11):78.
  • 19. Zielinski B, Sroka-Oleksiak A, Rymarczyk D, et al. Deep learning approach to describe and classify fungi microscopic images. PLoS One. 2020;15(6):e0234806.
  • 20. Singla N, Kundu R, Dey P. Artificial Intelligence: Exploring utility in detection and typing of fungus with futuristic application in fungal cytology. Cytopathology. 2024;35(2):226-34.
  • 21. Ma H, Yang J, Chen X, et al. Deep convolutional neural network: a novel approach for the detection of Aspergillus fungi via stereomicroscopy. Journal of Microbiology. 2021;59(6):563-72.
  • 22. Liu R, Liu T, Dan T, et al. AIDMAN: An AI-based object detection system for malaria diagnosis from smartphone thin-blood-smear images. Patterns (N Y). 2023;4(9):100806.
  • 23. Fisch D, Evans R, Clough B, et al. HRMAn 2.0: Next-generation artificial intelligence-driven analysis for broad host-pathogen interactions. Cellular Microbiology. 2021;23(7):e13349.
  • 24. Diab R. Artificial intelligence and Medical Parasitology: Applications and perspectives. Parasitologists United Journal. 2023;16(2):91-3.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Tıbbi Bakteriyoloji, Tıbbi Mikoloji, Tıbbi Parazitoloji, Tıbbi Viroloji, Tıbbi Mikrobiyoloji (Diğer)
Bölüm Araştırma Makaleleri
Yazarlar

Mert Kandilci 0009-0007-1548-3200

Gülfer Yakıcı 0000-0001-6486-3209

Mediha Begüm Kayar 0000-0002-9657-5970

Erken Görünüm Tarihi 4 Temmuz 2024
Yayımlanma Tarihi
Gönderilme Tarihi 25 Mart 2024
Kabul Tarihi 20 Mayıs 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 5 Sayı: 2

Kaynak Göster

Vancouver Kandilci M, Yakıcı G, Kayar MB. Artificial Intelligence and Microbiology. Exp Appl Med Sci. 2024;5(2):119-28.

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