A computer-implemented method for real-time prediction of microorganism growth or microorganism growth inhibition of a microorganism inoculum in a phenotypic antimicrobial susceptibility test, wherein the method comprises or consists of the following steps: a) Incubating a sample of a microorganism with a suitable incubating device, wherein the incubated sample comprises a single antimicrobial agent or a combination of antimicrobial agents, b) Taking a sequence of two or more digital images of the incubated sample microorganism of step a) with a suitable imaging device and providing information on pixel intensity ( p i) per pixel of the respective images, wherein the sequence of images is consecutively distributed within a progress value ( t p) with 0 < t p < 1, wherein the progress value t p represents a ratio of a proportional incubation time period for the consecutively distributed sequence of images of the incubated sample microorganism divided by an overall incubation time period from start to end of the incubation for the respective microorganism, and c) Predicting microorganism growth or microorganism growth inhibition for the incubated sample microorganism as a function of an output score ( S o) 0 ≤ S o ≤ 1 of a deep learning neural network configured to extract spatio-temporal features of the sequence of images and to classify the respective images, wherein S o = 0 represents a distinct microorganism growth inhibition and S o = 1 represents a distinct microorganism growth, and wherein the deep learning neural network uses as input data (i) the sequence of pixel intensity ( p i) per pixel for the two or more images of the incubated sample microorganism of step b), or (ii) a determined sequence of differences in the pixel intensity (Δ p i) per pixel between two consecutive images respectively for the two or more images of step b), or (iii) the sequence of pixel intensity ( p i) per pixel for the two or more images of the incubated sample microorganism of step b) and a determined sequence of differences in the pixel intensity (Δ p i) per pixel between two consecutive images respectively for the two or more images of step b). a) Incubating a sample of a microorganism with a suitable incubating device, wherein the incubated sample comprises a single antimicrobial agent or a combination of antimicrobial agents, b) Taking a sequence of two or more digital images of the incubated sample microorganism of step a) with a suitable imaging device and providing information on pixel intensity ( p i) per pixel of the respective images, wherein the sequence of images is consecutively distributed within a progress value ( t p) with 0 < t p < 1, wherein the progress value t p represents a ratio of a proportional incubation time period for the consecutively distributed sequence of images of the incubated sample microorganism divided by an overall incubation time period from start to end of the incubation for the respective microorganism, and c) Predicting microorganism growth or microorganism growth inhibition for the incubated sample microorganism as a function of an output score ( S o) 0 ≤ S o ≤ 1 of a deep learning neural network configured to extract spatio-temporal features of the sequence of images and to classify the respective images, wherein S o = 0 represents a distinct microorganism growth inhibition and S o = 1 represents a distinct microorganism growth, and wherein the deep learning neural network uses as input data (i) the sequence of pixel intensity ( p i) per pixel for the two or more images of the incubated sample microorganism of step b), or (ii) a determined sequence of differences in the pixel intensity (Δ p i) per pixel between two consecutive images respectively for the two or more images of step b), or (iii) the sequence of pixel intensity ( p i) per pixel for the two or more images of the incubated sample microorganism of step b) and a determined sequence of differences in the pixel intensity (Δ p i) per pixel between two consecutive images respectively for the two or more images of step b). (i) the sequence of pixel intensity ( p i) per pixel for the two or more images of the incubated sample microorganism of step b), or (ii) a determined sequence of differences in the pixel intensity (Δ p i) per pixel between two consecutive images respectively for the two or more images of step b), or (iii) the sequence of pixel intensity ( p i) per pixel for the two or more images of the incubated sample microorganism of step b) and a determined sequence of differences in the pixel intensity (Δ p i) per pixel between two consecutive images respectively for the two or more images of step b).