Method for optimizing an optical aid by automatically determining subjective visual performance by means of machine learning, comprising the following steps: a) providing at least one training dataset, b) training an artificial neural network by means of the at least one training dataset, c) generically creating a stimulus image, d) parameterizing the stimulus image, wherein a first and a second parameterization is performed, and wherein preferably the first parameterization of the stimulus image is implemented at least by way of an image size of the stimulus image, a stimulus contrast and/or a stimulus shape, e) providing a detection probability function, f) classifying the stimulus image by means of the trained neural network for providing a classification of the stimulus image, g) determining a detection probability for the first parameterization, performed in step d), on the basis of the classification of the stimulus image, h) adapting the first parameterization of the stimulus image on the basis of the determined detection probability, i) classifying the adapted stimulus image by means of the trained neural network for providing a classification of the adapted stimulus image, j) determining the detection probability for the first parameterization, adapted in step h), on the basis of the classification of the adapted stimulus image, k) repeating steps h) to j) until reaching a termination criterion with respect to finding a threshold of the subjective visual performance, l) determining the threshold of the subjective visual performance, m) repeatedly running through steps d) to 1) multiple times with a different second parameterization in each case, wherein each second parameterization of the stimulus image comprises the application of at least one property of the optical aid to the stimulus image, n) optimizing the optical aid by means of selecting the second parameterization with the highest subjective visual performance determined in step 1). a) providing at least one training dataset, b) training an artificial neural network by means of the at least one training dataset, c) generically creating a stimulus image, d) parameterizing the stimulus image, wherein a first and a second parameterization is performed, and wherein preferably the first parameterization of the stimulus image is implemented at least by way of an image size of the stimulus image, a stimulus contrast and/or a stimulus shape, e) providing a detection probability function, f) classifying the stimulus image by means of the trained neural network for providing a classification of the stimulus image, g) determining a detection probability for the first parameterization, performed in step d), on the basis of the classification of the stimulus image, h) adapting the first parameterization of the stimulus image on the basis of the determined detection probability, i) classifying the adapted stimulus image by means of the trained neural network for providing a classification of the adapted stimulus image, j) determining the detection probability for the first parameterization, adapted in step h), on the basis of the classification of the adapted stimulus image, k) repeating steps h) to j) until reaching a termination criterion with respect to finding a threshold of the subjective visual performance, l) determining the threshold of the subjective visual performance, m) repeatedly running through steps d) to 1) multiple times with a different second parameterization in each case, wherein each second parameterization of the stimulus image comprises the application of at least one property of the optical aid to the stimulus image, n) optimizing the optical aid by means of selecting the second parameterization with the highest subjective visual performance determined in step 1). <EOS>