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AI-Powered Microscopic Diagnostic Techniques for Candida albicans Detection: A Systematic Review
Reyhaneh Shoorgashti1, Farnaz Jafari2, Simin Lesan1
1Dept. of Oral and Maxillofacial Medicine, School of Dentistry, Islamic Azad University of Medical Sciences, Tehran, Iran.
Journal of Dentistry (Shiraz, Iran)
|April 17, 2026
Summary
Artificial intelligence (AI) can significantly improve the detection of Candida albicans (C. albicans) infections. AI-driven methods, particularly using microscopic images, offer enhanced accuracy and speed for earlier diagnosis and better patient outcomes.
Area of Science:
- Microbiology
- Medical Technology
- Computational Biology
Background:
- Candida albicans (C. albicans) infections pose a significant public health challenge due to rising incidence and resistance to conventional therapies.
- Artificial intelligence (AI) offers promising solutions for detecting C. albicans infections.
Purpose of the Study:
- This review synthesizes recent advancements in AI-driven microscopic detection of C. albicans.
- It explores methodologies and clinical implications of AI in identifying C. albicans infections.
Main Methods:
- A comprehensive literature search was performed across major scientific databases (PubMed, Scopus, Embase, Web of Science, Google Scholar).
- Seven relevant studies employing AI and machine learning (ML) for C. albicans detection were selected and analyzed.
Main Results:
- Microscopic images were the most common dataset for AI-based C. albicans detection.
- AI detection accuracy using microscopic images varied from 63% to 100%.
- One study achieved 97.70% accuracy using volatile organic compounds (smell fingerprint).
Conclusions:
- AI technologies can substantially enhance the accuracy, speed, and efficiency of C. albicans detection.
- AI provides crucial support for earlier infection identification and optimized treatment strategies.
- Improved detection through AI ultimately leads to better patient outcomes.
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