Automatic identification of clinically important Aspergillus species by artificial intelligence-based image recognition: proof-of-concept study

Automatic identification of clinically important Aspergillus species by artificial intelligence-based image recognition: proof-of-concept study

While morphological examination is the most widely used for Aspergillus identification in clinical laboratories, PCR–sequencing and MALDI–TOF MS are emerging technologies in more financially-competent laboratories. However, mycological expertise, molecular biologists and/or expensive equipment are n...

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Bibliographic Details
Main Authors: Chi-Ching Tsang, Chenyang Zhao, Yueh Liu, Ken P. K. Lin, James Y. M. Tang, Kar-On Cheng, Franklin W. N. Chow, Weiming Yao, Ka-Fai Chan, Sharon N. L. Poon, Kelly Y. C. Wong, Lianyi Zhou, Oscar T. N. Mak, Jeremy C. Y. Lee, Suhui Zhao, Antonio H. Y. Ngan, Alan K. L. Wu, Kitty S. C. Fung, Tak-Lun Que, Jade L. L. Teng, Dirk Schnieders, Siu-Ming Yiu, Susanna K. P. Lau, Patrick C. Y. Woo
Format: Article
Language:English
Published: Taylor & Francis Group 2025-12-01
Series:Emerging Microbes and Infections
Subjects:
Aspergillus
identification
artificial intelligence
machine learning
image recognition
automation
Online Access:https://www.tandfonline.com/doi/10.1080/22221751.2024.2434573

Internet

https://www.tandfonline.com/doi/10.1080/22221751.2024.2434573

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