Methods and systems for automating opthalmic diagnosis and treatment

A system for automated ophthalmic diagnostic classification using video ophthalmoscopy, comprising:

• an imaging device configured to acquire video frames and/or static images of a subject's optic disc during an ophthalmoscopic examination;
• an input module for receiving said video frames and/or static images;
• a neural network operatively connected to the input module, the neural network configured to:
• analyze the acquired video frames and/or static images using a machine learning model trained to segment optic nerve head features across varying image qualities and patient populations;
• measure a radial width of a neuroretinal rim by determining a shortest distance between an outer optic disc margin and an inner cup margin from the segmented optic nerve head features, wherein the measurement:
• substantially or completely excludes blood vessels extending beyond rim tissue;
• accounts for a circumferential extent of complete rim absence, if present;
• and
• is taken along a radial line extending from a center of the optic disc to a location where the rim is thinnest;
• calculate a Disc Damage Likelihood Scale (DDLS) score based on the measured neuroretinal rim width and an optic disc size;
• adjust the calculated DDLS score based on the optic disc size;
• and
• an output module configured to:
• provide real-time or substantially real-time DDLS classification during the ophthalmoscopic examination;
• generate a report indicating glaucoma risk assessment based on the DDLS score;
• and
• suggest clinical recommendations based on the risk assessment.