The use of AI-based techniques in healthcare are becoming more and more common and more disease-specific. Glaucoma is a disorder in eye that causes damage to the optic nerve which can lead to permanent blindness. It is caused by the elevated pressure inside the eye due to the obstruction to the flow of the drainage fluid (aqueous humor). Most recent treatment options involve minimally invasive glaucoma surgery (MIGS) in which a stent is placed to improve drainage of aqueous humor from the eye. Each MIGS surgery has a different mechanism of action, and the relative efficacy and chance of success is dependent on multiple patient-specific factors. Hence the ophthalmologists are faced with the critical question; which method would be better for a specific patient, both in terms of glaucoma control but also taking into consideration patient quality of life? In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) has been developed in the form of a Treatment Advice prediction system that will offer the clinician a suggested MIGS treatment from the baseline clinical parameters. ANFIS was used with a real-world MIGS data set which was a retrospective case series of 372 patients who underwent either of the four MIGS procedures from July 2016 till May 2020 at a single center in the UK.•Inputs used: Clinical measurements of Age, Visual Acuity, Intraocular Pressure (IOP), and Visual Field, etc.•Output Classes: iStent, iStent and Endoscopic Cyclophotocoagulation (ICE2), PreserFlo MicroShunt (PMS) and XEN-45).•Results: The proposed ANFIS system was found to be 91% accurate with high Sensitivity (80%) and Specificity (90%).
Keywords: Adaptive neuro-fuzzy classification; MIGS; Minimally invasive glaucoma surgery; Personalized medicine; iMIGS: ANFIS-based Prediction System.
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