Scientists from Tokyo Metropolitan University have used machine learning to automate the identification of defects in sister chromatid cohesion. They trained a convolutional neural network (CNN) with microscopy images of individual stained chromosomes, identified by researchers as having or not having cohesion defects. After training, it was able to successfully classify 73.1% of new images. Automation promises better statistics, and more insight into the wide range of disorders which cause cohesion defects.