Urrejola-Barrios, SebastiánDel Campo-Smith, MatíasDurán, EduardoAsahi, TakeshiOpitz, DanielaLobos-González, Lorena2023-02-212023-02-212022Urrejola-Barrios, S., del Campo-Smith, M., Duran, E., Asahi, T., Opitz, D., & Lobos-Gonzalez, L. Scratch Assay Image Analysis Automation. Abstract Track 26th MIUA conference University of Cambridge, UK., 106–109 (2022)https://repositorio.udd.cl/handle/11447/7028In this brief proof-of-concept paper, we present an algorithm developed in Python to automate the analysis of images obtained in scratch assays. Our algorithm uses random forest, a classic machine learning technique, to train and segment scratch assay images. This enables an average time reduction of 84% on the analysis of the images, together with a procedure with replicable results.enMachine learningMedical images & software solutionArticle