Automated analysis of optic nerve images for detection and staging of papilledema.

PURPOSE: To develop an automated system that analyzes digital fundus images for staging and monitoring of optic disc edema (i.e., papilledema), due to raised intracranial pressure.

METHODS: A total of 294 retrospective, digital photographs of the right and left eyes of 39 subjects with papilledema acquired over the span of 2 years were used. Software tools were developed to analyze three features of papilledema from digital fundus photographs: (1) sharpness of the optic disc border, (2) discontinuity along major vessels overlying the optic nerve, and (3) texture properties of the peripapillary retinal nerve fiber layer (RNFL). A classifier used these features to assign a grade of papilledema according to a standard protocol used by an expert neuro-ophthalmologist (RK).

RESULTS: The algorithm showed substantial agreement (κ = 0.71, P < 0.001) with the neuro-ophthalmologist when grading papilledema per patient. Vessel features showed statistical significance (P < 0.05) in differentiating grades 0, 1, and 2 from grades 3 and 4, whereas disc obscuration differentiated grades 0 or 1 from the rest (P < 0.05).

CONCLUSIONS: These results show that this algorithm can be used to automatically grade papilledema. The algorithm provides objective and quantitative assessment of the stage of papilledema with accuracy that is comparable to grading by a neuro-ophthalmologist. One application is in rapid assessment of digital optic nerve photographs acquired in clinical, intensive care, and emergency response settings by nonophthalmologists to evaluate for the presence and severity of papilledema, due to intracranial hypertension.