Artificial intelligence (AI) is transforming microbiology by enhancing the diagnosis of microorganisms and improving disease management, drug development, and antibiotic resistance prediction. AI systems, including machine learning models, automate and expedite processes such as sample processing and data interpretation, addressing challenges faced by traditional methods. Techniques like AI-enhanced microscopy and MALDI-TOF mass spectrometry improve accuracy and efficiency in microbial identification while alleviating the burden on skilled personnel.

1. Artificial Intelligence in
Identification of Microorganisms
DR. C P PRINCE
HOD & Associate Professor
Department of Microbiology
Mother Theresa Post Graduate & Research Institute of Health Sciences
(Government of Puducherry Institution)
Puducherry-605006

4. • When people hear the term artificial intelligence they often think
about killer robots and supercomputer overlord.
• There are certainly risks associated with artificial intelligence, such as
autonomous weapons and some features of social media.
• But artificial intelligence can do better than that.
• Artificial intelligence has already permeated many aspects of our
lives, including internet, self-driving cars, and healthcare.
• In microbiology, artificial intelligence has also been successfully
applied for a variety of tasks, including drug discovery and imaging
and diagnostics of microorganisms.
• As with many previous inventions, artificial intelligence can be applied
to do bad or good.
• It can free us from tedious and repetitive tasks, improve the quality of
healthcare, and transform our lives for the better.

5. Artificial Intelligence
• Artificial intelligence (AI) has been defined as the study of algorithms
that give machines the ability to reason and perform functions such
as problem solving, object and word recognition, inference of world
states, and decision-making.

6. Machine Learning
• Machine learning (ML) is a process in which computing systems learn
from data and use algorithms to execute tasks without being explicitly
programmed.

7. Deep Learning
• Deep learning (DL) is a type of ML approach that imitates the way
how brain gains knowledge with applied mathematics and statistics.

8. AI in Microbiology
• The diagnosis is an important factor in healthcare care, and it is essential to
identify microorganisms that cause infections and diseases.
• The application of artificial intelligence (AI) systems can improve disease
management, drug development, antibiotic resistance prediction, and
epidemiological monitoring in the field of microbial diagnosis.
• AI systems can quickly and accurately detect infections, including new and
drug-resistant strains, and enable early detection of antibiotic resistance
and improved diagnostic techniques.
• The application of AI in bacterial diagnosis focuses on the speed, precision,
and identification of pathogens and the ability to predict antibiotic
resistance.

9. Microbial diagnosis
• identification of microorganisms through techniques such as culture,
molecular analysis, and imaging.
• It starts with appropriate sample collection and runs into several
problems with conventional procedures, including sample handling,
difficulty in culture, incorrect identification, and antimicrobial
susceptibility testing difficulties.
• These traditional methods require manpower, and treatment is often
delayed

10. • One of the best examples is that machine learning models, such as
deoxyribonucleic acid (DNA) sequencer, can analyze the genomic
sequences of bacteria and viruses to predict their propensity for
mutations and resistance to specific drugs, guiding clinicians in
selecting the most effective treatment options .
• AI-powered automation speeds up processes such as sample
processing, image analysis, and data interpretation.
• This not only shortens the diagnostic turnaround time but also allows
healthcare professionals to concentrate on the most challenging
aspects of patient care

12. Artificial intelligence which became important in the laboratory is used
in medical microbiology in infectious disease testing to support
decision-making, identification and antimicrobial susceptibility testing
with Raman technologies, image analysis, and MALDI-TOF-MS.

13. Traditional microbial diagnosis
• delay in the initial phase of therapy, allowing diseases to progress
uncontrolled due to a delay in results
• Dependence on skilled personnel. Traditional methods rely on highly
qualified personnel, specialized equipment, and significant resources,
making them error-prone in settings with limited resources

14. Microscopy
• Microscopes enhanced with AI have the potential to aid
microbiologists’ examination of organisms and use the collected data
for diagnosis or root cause analysis.
• In a study, microbiologists at Beth Israel Deaconess Medical Centre
have demonstrated that an automated AI-enhanced microscope
system is “highly adept” at identifying images of bacteria quickly and
accurately

15. Google partners with Defense Department on
AI-enhanced microscope - SiliconANGLE

16. • Automated Detection of Streptococcus pyogenes Pharyngitis by Use
of Colorex Strep A CHROMagar and WASPLab Artificial Intelligence
Chromogenic Detection Module Software

17. Chromogenic agar

18. MALDI-TOF MS
• Matrix Assisted Laser Desorption Ionization-Time Of Flight- Mass
Spectrometry

19. Conventional; culture on microbiological
media and identification by biochemical tests
• Sensitive
• Inexpensive
• Lengthy and time consuming process
• Might require 24–48 h

20. Immunological-based methods
• Faster than conventional methods
• Can detect both contaminating organisms and their toxins
• Not as specific, sensitive, and rapid as nucleic-acid based detection
methods
• Require large amounts of antigen
• Developed for only a small number of microorganisms

21. Florescent in situ hybridization (FISH)
• Rapid detection and identification directly from slide smears
• Fast and ease-of use of conventional staining methods combined with
specificity of molecular methods
• Test limited by the availability of specific antigens for detection

22. Molecular based methods
(i)Real-time PCR
(ii)Multiplex-PCR
• Culturing of the sample is not required
• Specific, sensitive, rapid, and accurate
• Closed-tube system reduces the risk of contamination
• Can detect many pathogens simultaneously
• A highly precise thermal cycler is needed
• Trained laboratory personnel required for performing the test

23. DNA sequencing
• 16S rDNA and 18S rDNA sequencing are the gold standards
• Can identify fastidious and uncultivable microorganisms
• Trained laboratory personnel and powerful interpretation softwares
are required
• Expensive
• Not suitable for routine clinical use

24. Microarrays
• Large scale screening system for simultaneous diagnosis and detection of
many pathogens
• Trained laboratory personnel and powerful interpretation softwares are
required
• Expensive
• Trained laboratory personnel required

25. Loop-mediated isothermal amplification (LAMP)
assay
• Can generate large copies of DNA in less than an hour
• No sophisticated equipment is required
• Trained laboratory personnel and powerful interpretation softwares
are required
• Expensive
• Trained laboratory personnel required

26. Metagenomic assay
• Useful for random detection of pathogens
• Data acquisition and data analysis is time consuming
• Trained laboratory personnel required

27. MALDI-TOF MS
• Fast
• Accurate
• Less expensive than molecular and immunological-based detection
methods
• Trained laboratory personnel not required
• High initial cost of the MALDI-TOF equipment

29. Thanks
AI generated Lab gloves holding HIV virus organism AI generated biotechnology close-up image