A Functional Regression Model of the Retinal Nerve Fiber Layer Thickness in Healthy Subjects.

ABSTRACT:

Purpose

A new functional regression model is presented to explain the intersubject variability of the circumpapillary retinal nerve fiber layer (RNFL) thickness in healthy subjects.

Methods

To evaluate the functional regression approach we used data from 202 healthy volunteers, divided equally into training samples (TS) and validation samples (VS). Covariates included RNFL, fovea distance, fovea angle, optic disk ratio, orientation and area provided by Fourier-domain-optical coherence tomography, age, and refractive error. Root mean square errors (RMSE) were calculated for each of the 256 sectors and for the 12 clock-hour sectors in the TS and VS and were compared to the RMSE of the previous model and the standard deviation of the raw data.

Results

With the functional

SUBMITTER: Pereira I 

PROVIDER: S-EPMC5777176 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature