Abstract: A method for training and using a machine learning system for a differentiation between healthy and diseased tissue during a microsurgical intervention is described. In this case, the method comprises: receiving training data and associated annotation data for training a machine learning system, training the machine learning system, which after training is configured for a prediction of a probability value and a prediction of a trustworthiness value, from which a control signal for a surgery assistance system is derivable, which is usable during a later application during a microsurgical operation, and storing parameter values of the trained machine learning model.

Abstract: A computer-implemented method for generating a control signal by locating at least one instrument by way of a combination of machine learning systems on the basis of digital images is described. In this case, the method includes determining parameter values of a movement context by using the at least two digital images and determining an influence parameter value which controls an influence of one of the digital images and the parameter values of the movement context on the input data which are used within a first trained machine learning system, which has a first learning model, for generating the control signal.

Abstract: A computer-implemented method for predicting digital images in the form of a digital fluorescence representation together with a further derived representation by means of a combined machine learning system is described. The method comprises providing a first digital image of a tissue sample that was recorded under white light by means of a microsurgical optical system with a digital image recording unit, and predicting a second digital image of the tissue sample in a fluorescence representation and a further representation, which has optical indications about diseased tissue elements. This is done by means of a previously trained combined machine learning system comprising a trained combined machine learning model for predicting the second digital image of the tissue sample in the fluorescence representation and the further representation.