Fuzzy Logic-Based AI Model for Accurate Grading of Papilledema Severity

Papilledema is characterized by optic disc swelling due to increased intracranial pressure, necessitates accurate diagnostic tools for effective treatment planning. This study presents an innovative artificial intelligence (AI) model employing fuzzy logic to grade papilledema severity. The study utilized a newly collected dataset containing different clinical measurements. The input of the system includes Best-Corrected Visual Acuity (BCVA), pupil reaction, retinal nerve fiber layer (RFNL) thickness, total peripapillary thickness, and visual field measurements and output of the system is to identify the level of severity of papilledema within four grades. The fuzzy logic model achieves a classification accuracy exceeding 90%, demonstrating its effectiveness in discerning diverse papilledema severity levels. Comparative analysis with existing literature highlights competitive performance, positioning the model as a promising neuro-ophthalmological diagnostic tool. Unique contributions include dataset composition, feature extraction, and the model's implementation. This study pioneers in advancing diagnostic methodologies for papilledema, offering a reliable tool for ophthalmologists used for the first time in papilledema severity detection. The results indicate the model's potential for integration into clinical practice, contributing AI-driven neuro-ophthalmological diagnostics and enhancing patient care.