Rapid Screening Accuracy: Machine Learning-Based Detection System for Species Identification of Aspergillus from Human Clinical Samples Compare with Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometer
Worada Samosornsuk, Pradya Prempraneerach, Chollanant Khattiyawech and Panarat Hematulin
1Department of Medical Technology, Faculty of Allied Health Sciences, Thammasat University, Pathum Thani, 12120, Thailand
2Department of Mechanical Engineering, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathum Thani, 12120, Thailand
3Microbiology Laboratory Unit, Thammasat University Hospital, Pathum Thani, 12120, Thailand
Abstract
Publication details
Received: 06 Dec 2024
Revised: 16 Jan 2025
Accepted: 20 Jan 2025
Published online: 07 Mar 2025
Article type:
Research Paper
DOI:
10.30919/es1441
Citation:
Engineered Science, , ,
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26
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Description:
Machine learning enables rapid, accurate Aspergillus species identification from colony images, achi....
Machine learning enables rapid, accurate Aspergillus species identification from colony images, achieving 96% accuracy, validated against matrix-assisted laser desorption/ionization time-of-flight mass spectrometer.
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