Multimodal prediction of visual acuity response

A system for predicting visual acuity response, the system comprising:

• a non-transitory memory;
• and
• one or more processors coupled to the non-transitory memory and configured to read instructions from the non-transitory memory to cause the system to perform operations comprising:
• receiving a first input that includes two-dimensional imaging data associated with a subject undergoing a treatment;
• receiving a second input that includes three-dimensional imaging data associated with the subject undergoing the treatment;
• and
• predicting, via a neural network system, a visual acuity response (VAR) output using the first input and the second input, the VAR output comprising a predicted change in visual acuity of the subject undergoing the treatment, wherein the neural network system comprises:
• a first neural network sub-system comprising at least one first input layer and at least one first dense inner layer, the at least one first input layer configured to receive the first input, the at least one first dense inner layer configured to apply a first trained model to the first input layer;
• a second neural network sub-system comprising at least one second input layer and at least one second dense inner layer, the at least one second input layer configured to receive the second input, the at least one second dense inner layer configured to apply a second trained model to the second input layer;
• and
• a third neural network sub-system comprising at least one third dense inner layer configured to receive a first output from the at least first dense inner layer and a second output from the at least second dense layer and to apply a third trained model to the first and second outputs to thereby predict the VAR output.

A non-transitory, machine-readable medium having stored thereon machine-readable instructions executable to cause a system to perform operations comprising:

• receiving a first input that includes two-dimensional imaging data associated with a subject undergoing a treatment;
• receiving a second input that includes three-dimensional imaging data associated with the subject undergoing the treatment;
• and
• predicting, via a neural network system, a visual acuity response (VAR) output using the first input and the second input, the VAR output comprising a predicted change in visual acuity of the subject undergoing the treatment, wherein the neural network system comprises:
• a first neural network sub-system comprising at least one first input layer and at least one first dense inner layer, the at least one first input layer configured to receive the first input, the at least one first dense inner layer configured to apply a first trained model to the first input layer;
• a second neural network sub-system comprising at least one second input layer and at least one second dense inner layer, the at least one second input layer configured to receive the second input, the at least one second dense inner layer configured to apply a second trained model to the second input layer;
• and
• a third neural network sub-system comprising at least one third dense inner layer configured to receive a first output from the at least first dense inner layer and a second output from the at least second dense layer and to apply a third trained model to the first and second outputs to thereby predict the VAR output.