  Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique in
biomedical research that uses the fluorophore decay rate to provide additional
contrast in fluorescence microscopy. However, at present, the calculation,
analysis, and interpretation of FLIM is a complex, slow, and computationally
expensive process. Machine learning (ML) techniques are well suited to extract
and interpret measurements from multi-dimensional FLIM data sets with
substantial improvement in speed over conventional methods. In this topical
review, we first discuss the basics of FILM and ML. Second, we provide a
summary of lifetime extraction strategies using ML and its applications in
classifying and segmenting FILM images with higher accuracy compared to
conventional methods. Finally, we discuss two potential directions to improve
FLIM with ML with proof of concept demonstrations.
