{Reference Type}: Journal Article
{Title}: SD-OCT parameters and visual field defect in chiasmal compression and the diagnostic value of neural network model.
{Author}: Jeon H;Park KH;Kim H;Choi H;
{Journal}: Eur J Ophthalmol
{Volume}: 31
{Issue}: 5
{Year}: Sep 2021
{Factor}: 1.922
{DOI}: 10.1177/1120672120947593
{Abstract}: <B>UNASSIGNED: </B>To evaluate the peripapillary retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) measurements using spectral domain optical coherence tomography (SD-OCT) in patients with chiasmal compression and analyze the diagnostic value of a neural network model.<BR><B>UNASSIGNED: </B>Forty-seven patients with chiasmal compressive disorder were recruited and divided into two groups depending on the visual field defect (perimetric; group 1 and preperimetric; group 2). Fifty-seven normal subjects were also recruited (group 3). Peripapillary RNFL and macular GCIPL were analyzed in each group. A multilayer perceptron was trained using a training dataset and derived a neural network model. The diagnostic performances were compared using the area under the receiver operating curve (AUROC) between each parameters and neural network model.<BR><B>UNASSIGNED: </B>All macular GCIPL parameters, except inferotemporal GCIPL thickness, were thinner in group 1 than in group 2 and group 3, with barely any difference between group 2 and group 3 parameter values. The diagnostic power of the neural network model, minimum GCIPL, and inferonasal GCIPL were superior when compared with other parameters; the diagnostic values of these three parameters are not significantly different in discriminating the patients and normal control. However, the neural network exhibited the best diagnostic power in distinguishing group 2 and group 3.<BR><B>UNASSIGNED: </B>Macular GCIPL was reduced in chiasmal compression patients with visual field defect which was not evident in the preperimetric state. Neural network model showed superior diagnostic value in discriminating the preperimetric patients from normal control. The results suggest that neural networks may be helpful in the early diagnosis of chiasmal compression.