Method for detecting a binding of autoantibodies from a patient sample to double-stranded deoxyribonucleic acid using Crithidia luciliae cells by means of fluorescence microscopy and by means of digital image processing, comprising - provision of a substrate which has multiple Crithidia luciliae cells, - incubation of the substrate with the patient sample which potentially has the autoantibodies, - incubation of the substrate with secondary antibodies which have each been labelled with a fluorescent dye which is preferably a green fluorescent dye, - acquisition of a fluorescence image of the substrate in a colour channel which corresponds to the fluorescent dye, wherein the colour channel is preferably a green channel, characterized by - identification by means of a first pretrained convolutional neural network of respective sub-images in the one fluorescence image that each represent at least one Crithidia luciliae cell, - respective processing of at least one subset of the respective sub-images by means of a second pretrained convolutional neural network whereby the second convolutional neural network determines a respective subordinate image representing the kinetoplast region for a respective sub-image for determining respective binding measures (IBM1, IBM2) on the basis of respective subordinate images, whereby a respective binding measure indicates a respective extent of a binding of autoantibodies in a respective kinetoplast region of a respective Crithidia luciliae cell of a respective sub-image, - determination of an overall binding measure with regard to the binding of autoantibodies from the patient sample to double-stranded deoxyribonucleic acid on the basis of the respective binding measures (IBM1, IBM2). comprising - provision of a substrate which has multiple Crithidia luciliae cells, - incubation of the substrate with the patient sample which potentially has the autoantibodies, - incubation of the substrate with secondary antibodies which have each been labelled with a fluorescent dye which is preferably a green fluorescent dye, - acquisition of a fluorescence image of the substrate in a colour channel which corresponds to the fluorescent dye, wherein the colour channel is preferably a green channel, - provision of a substrate which has multiple Crithidia luciliae cells, - incubation of the substrate with the patient sample which potentially has the autoantibodies, - incubation of the substrate with secondary antibodies which have each been labelled with a fluorescent dye which is preferably a green fluorescent dye, - acquisition of a fluorescence image of the substrate in a colour channel which corresponds to the fluorescent dye, wherein the colour channel is preferably a green channel, characterized by - identification by means of a first pretrained convolutional neural network of respective sub-images in the one fluorescence image that each represent at least one Crithidia luciliae cell, - respective processing of at least one subset of the respective sub-images by means of a second pretrained convolutional neural network whereby the second convolutional neural network determines a respective subordinate image representing the kinetoplast region for a respective sub-image for determining respective binding measures (IBM1, IBM2) on the basis of respective subordinate images, whereby a respective binding measure indicates a respective extent of a binding of autoantibodies in a respective kinetoplast region of a respective Crithidia luciliae cell of a respective sub-image, - determination of an overall binding measure with regard to the binding of autoantibodies from the patient sample to double-stranded deoxyribonucleic acid on the basis of the respective binding measures (IBM1, IBM2). - identification by means of a first pretrained convolutional neural network of respective sub-images in the one fluorescence image that each represent at least one Crithidia luciliae cell, - respective processing of at least one subset of the respective sub-images by means of a second pretrained convolutional neural network whereby the second convolutional neural network determines a respective subordinate image representing the kinetoplast region for a respective sub-image for determining respective binding measures (IBM1, IBM2) on the basis of respective subordinate images, whereby a respective binding measure indicates a respective extent of a binding of autoantibodies in a respective kinetoplast region of a respective Crithidia luciliae cell of a respective sub-image, - determination of an overall binding measure with regard to the binding of autoantibodies from the patient sample to double-stranded deoxyribonucleic acid on the basis of the respective binding measures (IBM1, IBM2).