Glaucoma detection using adaptive neuro-fuzzy inference system

Purpose. To develop an automated classifier based on adaptive neuro-fuzzy inference system (ANFIS) to differentiate between normal and glaucomatous eyes from the quantitative assessment of summary data reports of the Strains optical coherence tomography (OCT) in Taiwan Chinese population. Methods. This observational non-interventional, cross-sectional, case-control study included one randomly selected eye from each of the 341 study participants (135 patients with glaucoma and 206 healthy controls). Measurements of glaucoma variables (retinal nerve fiber layer thickness and optic nerve head topography) were obtained by Stratus OCT. Decision making was performed in two stages: feature extraction using the orthogonal array and the selected variables were treated as the feeder to adaptive neuro-fuzzy inference system (ANFIS), which was trained with the back-propagation gradient descent method in combination with the least squares method. With the Strains OCT parameters used as input, receiver operative characteristic (ROC) curves were generated by ANFIS to classify eyes as either glaucomatous or normal. Results. The mean deviation was -0.67 +/- 0.62 dB in the normal group and -5.87 +/- 6.48 dB in the glaucoma group (P < 0.0001). The inferior quadrant thickness was the best individual parameter for differentiating between normal and glaucomatous eyes (ROC area, 0.887). With ANFIS technique, the ROC area was increased to 0.925. Conclusions. With Stratus OCT parameters used as input, the results from ANFIS showed promise for discriminating between glaucomatous and normal eyes. ANFIS may be preferable since the output concludes the if-then rules and membership functions, which enhances the readability of the output. (C) 2005 Elsevier Ltd. All rights reserved.