AI-Powered Microscopic Diagnostic Techniques for Candida Albicans Detection: A Systematic Review

Shoorgashti, Reyhaneh; Jafari, Farnaz; Lesan, Simin

doi:10.30476/dentjods.2025.104629.2540

AI-Powered Microscopic Diagnostic Techniques for Candida Albicans Detection: A Systematic Review

Document Type : Systematic Review

Authors

Reyhaneh Shoorgashti ¹

Farnaz Jafari ²

Simin Lesan ¹

¹ Dept. of Oral and Maxillofacial Medicine, School of Dentistry, Islamic Azad University of Medical Sciences, Tehran, Iran.

² Oral and Dental Diseases Research Center, Kerman University of Medical Sciences, Kerman, Iran.

https://doi.org/10.30476/dentjods.2025.104629.2540

Abstract

Background: Artificial intelligence (AI) powered technologies can help detect Candida albicans (C. albicans) infections, which are a public health challenge due to increasing incidence rates and conventional therapy resistance.
Purpose: This review explores recent advancements, methodologies, and clinical implications in the AI-driven microscopic detection of C. albicans.
Materials and Method: A literature search was conducted across multiple databases, including PubMed, Scopus, Embase, Web of Science, and Google Scholar. Following a thorough review of the retrieved articles, 7 studies were selected for inclusion in this review.
Results: This review analyzed 7 studies that employed AI and machine learning (ML) to detect the presence of C. albicans. The most commonly used dataset for detecting C. albicans through AI was microscopic images. Two studies employed time-lapse microscopy, and another study used the microorganism's smell fingerprint or volatile organic compounds with an impressive accuracy of 97.70%. The accuracy of detecting C. albicans through AI using microscopic images ranged from 63% to 100% depending on the model used.
Conclusion: AI can improve the detection of C. albicans infections. It can enhance the accuracy, speed, and efficiency of detection, providing clinicians with invaluable support in identifying infections earlier, optimizing treatment strategies, and ultimately improving patient outcomes.

Keywords

Artificial Intelligence

Candida albicans

Deep Learning

Machine Learning

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