Deep Learning-Facilitated Study of the Rate of Change in Photoreceptor Outer Segment Metrics in RPGR-Related X-Linked Retinitis Pigmentosa

Abstract

Purpose: The aim of this retrospective cohort study was to obtain three-dimensional (3D) photoreceptor outer segment (OS) metrics measurements with the assistance of a deep learning model (DLM) and to evaluate the longitudinal change in OS metrics and associated factors in retinitis pigmentosa GTPase regulator (RPGR) X-linked retinitis pigmentosa (XLRP).

Methods: The study included 34 male patients with RPGR-associated XLRP who had preserved ellipsoid zone (EZ) within their spectral-domain optical coherence tomography volume scans and an approximate 2-year or longer follow-up. Volume scans were segmented using a DLM with manual correction for EZ and apical retinal pigment epithelium (RPE). OS metrics were measured from 3D EZ-RPE layers of volume scans. Linear mixed-effects models were used to calculate the rate of change in OS metrics and the associated factors, including baseline age, baseline OS metrics, and follow-up duration.

Results: The mean (standard deviation) of progression rates were -0.28 (0.43) µm/y, -0.73 (0.61) mm2/y, and -0.014 (0.012) mm3/y for OS thickness, EZ area, and OS volume, respectively. In multivariable analysis, the progression rates of EZ area and OS volume were strongly associated with their baseline values, with faster decline in eyes with larger baseline values (P ≤ 0.003), and nonlinearly associated with the baseline age (P ≤ 0.003). OS thickness decline was not associated with its baseline value (P = 0.32).

Conclusions: These results provide evidence to support using OS metrics as biomarkers to assess the progression of XLRP and as the outcome measures of clinical trials. Given that their progression rates are dependent on their baseline values, the baseline EZ area and OS volume should be considered in the design and statistical analysis of future clinical trials. Deep learning may provide a useful tool to reduce the burden of human graders to analyze OCT scan images and to facilitate the assessment of disease progression and treatment trials for retinitis pigmentosa.

Figure 1.

Examples of OS metric measurements. The data were obtained from the left eye of patient P22. (Top row) Volume scan obtained at the baseline. (Bottom row) Volume scan obtained 10.6 years after the baseline visit. (A, D) OS thickness (red area) on the mid-line B-scans from the volume scans. (B, E) Preserved EZ area overlapped with infrared fundus images; (C, F) OS volume maps. Note that, in the volume maps, the z-axis is in micrometers, different from the x-axis and y-axis, which are in millimeters.

Figure 2.

OS metric measurements obtained using the DLM with manual correction as a function of the age at the time when the OCT image was obtained. (A) Mean OS thickness; (B) EZ or OS area; and (C) OS volume. Only the data obtained from the right eyes are shown in this figure (n = 33). One patient had only the left eye included in the study because the right eye did not have 2-year follow-up before treatment. Error bars indicate ±1 SD of two OS metric measurements from the manual correction of DLM segmentation by two human graders. To reduce the cluster introduced by the error bars and to improve the visualization, (A) shows only the representative error bars for a few selected patients. “P” in the figure refers to individual patient.

Figure 3.

Mean OS metric measurements versus follow-up time in years. (A) Mean OS thickness; (B) mean EZ area; and (C) mean OS volume. Zero on the horizontal axis represents the baseline visit. The vertical axis scale is about a third of that in Figure 2 for the corresponding OS metric. The dashed lines and formulas are linear regressions fit to the data. The number of patients in each follow-up year was 34 (66 eyes) for the baseline visit, 27 (51 eyes) for the year 1 visit, 25 (47 eyes) for the year 2 visit, and 18 (34 eyes) for the year 3 visit. Error bars indicate ±1 SE of mean OS metric measurements. R² is the coefficient of determination for the association between the OS metrics and follow-up time.

Figure 4.

Progression rate of EZ area (A) and its square root (B), as well as OS volume (C) and its cube root (D) as a function of their baseline values for all eyes (n = 66). The rates of progression were obtained from the multivariate linear mixed-effects model analysis conducted on the average of two measurements from the DLM segmentation with manual correction by two graders. Solid lines represent the linear fit to the data.

Figure 5.

Bland–Altman plots of differences in the OS metric measurements between the DLM only and the DLM with manual correction (vertical axis, DLM only minus DLM with manual correction) versus their mean (horizontal axis) for mean OS thickness (A), EZ area (B), and OS volume (C). Because different OS metrics have different units, the scale (or the range) of the vertical axis in each plot was set to equal to that of its horizontal axis, so that the difference was “normalized” to the range of each OS metric measurement for easy comparison. The text inside each part of the figure lists the values of mean difference (Mean Diff), standard deviation of the difference (SD), and coefficient of repeatability (CoR, defined as 1.96 times SD of the difference). Dashed horizontal lines represent ±95% limits of agreement (mean ± CoR). Dotted horizontal lines represent the mean difference.