Recent Trends of Fluorescence Lifetime Imaging Microscopy Analysis Using Machine Learning

Fluorescence Lifetime Imaging Microscopy (FLIM) is an advanced imaging technique that provides quantitative information about molecular interactions and changes in the microenvironment by measuring the emission decay lifetimes of molecules undergoing fluorescence. The integration of FLIM with machine learning holds great promise for advancing biomedical research and clinical diagnostics by enabling precise quantification of cellular processes and microenvironmental changes. As FLIM instrumentation becomes more accessible and analytical methods continue to improve, the technique is poised to have a significant impact across various fields of biology and medicine. This review considers a range of methods of applying Machine Learning to FLIM data processing.