Neural Network Analysis for the detection of glaucomatous damage

• We study the detection problem of glaucomatous damage.
• We propose a neural network approach coupled with the optical coherence tomography.
• We found that the Receiver Operating Characteristic Curve can be improved significantly.

Glaucoma is a major cause of blindness and is prevalent among Asian populations. Therefore, early detection is of paramount importance in order to let patients have early treatments. One prominent indicator of glaucomatous damage is the Retinal Nerve Fiber Layer (RNFL) profile. In this paper, the performance of artificial neural network models in identifying RNFL profile of glaucoma suspect and glaucoma subjects is studied. RNFL thickness was measured using optical coherence tomography (Stratus OCT). Inputs to the neural network consisted of regional RNFL thickness measurements over 12 clock hours. Sensitivity and specificity for glaucoma detection will be compared by the area under the Receiver Operating Characteristic Curve (AROC). The results show that artificial neural network coupled with the OCT technology enhances the diagnostic accuracy of optical coherence tomography in differentiating glaucoma suspect and glaucoma from normal individuals.

In this paper, the performance of artificial neural network models in identifying RNFL profile of glaucoma suspect and glaucoma. RNFL thickness was measured using optical coherence tomography (Stratus OCT). Inputs to the neural network consisted of regional RNFL thickness measurements over 12 clock hours. Sensitivity and specificity for glaucoma detection will be compared by the area under the Receiver Operating Characteristic Curve (AROC): the results show that artificial neural network coupled with the OCT technology enhances the diagnostic accuracy of optical coherence tomography in differentiating glaucoma suspect and glaucoma from normal individuals.Figure optionsDownload as PowerPoint slide