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13 March 2024 Machine learning to advance fluorescence lifetime imaging analysis and applications
Alex Walsh, Linghao Hu, Blanche ter Hofstede, Daniela De Hoyos Canales
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Proceedings Volume PC12833, Design and Quality for Biomedical Technologies XVII; PC128330H (2024) https://doi.org/10.1117/12.3009339
Event: SPIE BiOS, 2024, San Francisco, California, United States
Abstract
Fluorescence lifetime imaging measures the time a fluorophore remains in the excited state before returning to the ground state. Fluorescence lifetime measurements provide environmental information about fluorophores. For the endogenous metabolic co-enzymes reduced nicotinamide adenine dinucleotide (NADH) and flavin adenine dinucleotide (FAD), the fluorescence lifetime is different for free and protein-bound molecules; thus, lifetime imaging provides metabolic information about cells in a label-free manner. However, fluorescence lifetime analysis via traditional decay fitting methods is time-consuming and requires expertise. Furthermore, direct relationships between lifetime metrics and cell metabolism or functions are difficult to define. Recent advances in the combination of machine learning and fluorescence lifetime imaging allow accelerated image analysis and cell phenotype identification. Guidelines for ensuring rigor and transference of machine learning models will be discussed in the context of the development and testing of machine learning models that identify metabolism pathway use of individual cells from autofluorescence lifetime images.
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Alex Walsh, Linghao Hu, Blanche ter Hofstede, and Daniela De Hoyos Canales "Machine learning to advance fluorescence lifetime imaging analysis and applications", Proc. SPIE PC12833, Design and Quality for Biomedical Technologies XVII, PC128330H (13 March 2024); https://doi.org/10.1117/12.3009339
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KEYWORDS
Machine learning
Fluorescence lifetime imaging
Fluorescence
Fluorophores
Mode conditioning cables
Nicotinamide adenine dinucleotide
Ground state
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