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.

原子力显微镜（AFM）图像是与机器学习分析最匹配的显微镜技术之一。AFM图像的数字格式使其能够在机器学习算法中直接使用，无需额外处理。此文论述了使用相对较少的AFM图像和小型数据库进行机器学习识别/分类的方法，同时讨论了除了流行的深度学习神经网络之外的机器学习方法。该方法已成功用于分析和分类生物细胞表面，并可应用于识别医疗图像、特定材料处理、法医研究甚至艺术真伪鉴定。提供了适用于AFM的机器学习分析的通用模板，以及对细胞表型识别的具体示例。特别关注所得结果的统计显著性分析，这在处理机器学习的论文中经常被忽视，同时提供了一种寻找统计显著性的简单方法。

关于原子力显微镜图像的机器学习分析：图像分类与样品表面识别