Abstract: A computing system generates a plurality of training datasets from past shoulder surgery cases and uses the plurality of training datasets to train a neural network. Each output layer neuron in a plurality of output layer neurons of the neural network corresponds to a different class in one or more shoulder pathology classification systems. The computing system may obtain a current input vector that corresponds to a current patient. Additionally, the computing system may apply the neural network to the current input vector to generate a current output vector. The computing system may determine, based on the current output vector, a recommended type of shoulder surgery for the current patient.

Abstract: A computing system generates a plurality of training datasets from past shoulder surgery cases and uses the plurality of training datasets to train a neural network. Each output layer neuron in a plurality of output layer neurons of the neural network corresponds to a different class in one or more shoulder pathology classification systems. The computing system may obtain a current input vector that corresponds to a current patient. Additionally, the computing system may apply the neural network to the current input vector to generate a current output vector. The computing system may determine, based on the current output vector, a classification of a shoulder condition of the current patient.