  Atomic force microscopy (AFM or SPM) imaging is one of the best matches with
machine learning (ML) analysis among microscopy techniques. The digital format
of AFM images allows for direct utilization in ML algorithms without the need
for additional processing. Additionally, AFM enables the simultaneous imaging
of distributions of over a dozen different physicochemical properties of sample
surfaces, a process known as multidimensional imaging. While this wealth of
information can be challenging to analyze using traditional methods, ML
provides a seamless approach to this task. However, the relatively slow speed
of AFM imaging poses a challenge in applying deep learning methods broadly used
in image recognition. This Prospective is focused on ML
recognition/classification when using a relatively small number of AFM images,
small database. We discuss ML methods other than popular deep-learning neural
networks. The described approach has already been successfully used to analyze
and classify the surfaces of biological cells. It can be applied to recognize
medical images, specific material processing, in forensic studies, even to
identify the authenticity of arts. A general template for ML analysis specific
to AFM is suggested, with a specific example of the identification of cell
phenotype. Special attention is given to the analysis of the statistical
significance of the obtained results, an important feature that is often
overlooked in papers dealing with machine learning. A simple method for finding
statistical significance is also described.
