Identifying/classifying damage features on soft materials, such as tissues, is much more challenging than on classical, hard materials—but nevertheless important, especially in the field of bio-tribology. For instance, cartilage samples from osteoarthritic patients exhibit surface damage even at early stages of tissue degeneration, and corneal tissues can be damaged by contact lenses when the ocular lubrication system fails. Here, we employ unsupervised machine learning (ML) methods to assess the surface condition of a soft tissue by detecting and classifying different wear morphologies as well as the severity of surface damage they represent. We show that different clustering methods, especially a k-means clustering algorithm, can indeed achieve a—from a material science point of view—meaningful classification of those tissue samples. Our study pinpoints the ability of unsupervised ML models to guide or even replace human decision processes for the analysis of complex surfaces and topographical datasets that—either owing to their complexity or the sample size—exceed the capability of the human brain.     «

Identifying/classifying damage features on soft materials, such as tissues, is much more challenging than on classical, hard materials—but nevertheless important, especially in the field of bio-tribology. For instance, cartilage samples from osteoarthritic patients exhibit surface damage even at early stages of tissue degeneration, and corneal tissues can be damaged by contact lenses when the ocular lubrication system fails. Here, we employ unsupervised machine learning (ML) methods to assess th...     »