Systems, apparatus and methods for treatment of retinal and macular diseases using artificial intelligence

An optical coherence tomography (OCT) device, comprising:

• a sensor configured to detect a level of macular edema or retinal exudation in an eye;
• a memory storing computer-executable instructions;
• and
• a processor coupled to the memory and configured to execute the computer executable instructions to cause the OCT device to:
• perform optical coherence tomography (OCT) on a patient to quantify an initial level of macular edema or retinal exudation of a patient having a retinal or macular disease;
• receive treatment information for a series of anti-vascular endothelial growth factor (anti-VEGF) injections to the patient over a period of time;
• perform additional periodic OCT on the patient subsequent to each anti-VEGF injection to determine treatment response as assessed by subsequent levels of macular edema or retinal exudation;
• collect a set of patient data based on the patient, the patient data including:
• the initial level of macular edema or retinal exudation, the subsequent levels of macular edema or retinal exudation, patient information, and treatment information;
• and
• apply the patient data for the patient, including the subsequent levels of macular edema and the treatment information, to a machine-learning model trained on a plurality of sets of training data including patient for a plurality of patients to determine a treatment plan for the retinal or macular disease of the patient after the series of anti-VEGF injections.

A computer network comprising:

• a memory storing computer-executable instructions;
• and
• a processor coupled to the memory and configured to execute the computer-executable instructions to cause the processor to:
• receive, from a plurality of optical coherence tomography (OCT) devices, training data comprising, for each of a plurality of patients having a retinal or macular disease:
• an initial level of macular edema or retinal exudation, a subsequent level of macular edema or retinal exudation after each of a series of anti-vascular endothelial growth factor (Anti-VEGF) injections to the patient over a period of time, patient information, and treatment information;
• train a machine-learning model on the training data to select a treatment plan that optimizes a predicted visual acuity of a patient based on a patient data set including:
• an initial level of macular edema for the patient, a subsequent level of edema after each of an initial series of anti-VEGF injections to the patient over a period of time, patient medical information, and patient treatment information;
• and
• apply the patient data set for the patient, including the subsequent level of edema after each of an initial series of anti-VEGF injections and the patient treatment information, to the machine-learning model to determine the treatment plan for the retinal or macular disease of the patient after the initial series of anti-VEGF injections.