A Deep Learning Approach to Improve Retinal Structural Predictions and Aid Glaucoma Neuroprotective Clinical Trial Design

Abstract

Purpose: To investigate the efficacy of a deep learning regression method to predict macula ganglion cell-inner plexiform layer (GCIPL) and optic nerve head (ONH) retinal nerve fiber layer (RNFL) thickness for use in glaucoma neuroprotection clinical trials.

Design: Cross-sectional study.

Participants: Glaucoma patients with good quality macula and ONH scans enrolled in 2 longitudinal studies, the African Descent and Glaucoma Evaluation Study and the Diagnostic Innovations in Glaucoma Study.

Methods: Spectralis macula posterior pole scans and ONH circle scans on 3327 pairs of GCIPL/RNFL scans from 1096 eyes (550 patients) were included. Participants were randomly distributed into a training and validation dataset (90%) and a test dataset (10%) by participant. Networks had access to GCIPL and RNFL data from one hemiretina of the probe eye and all data of the fellow eye. The models were then trained to predict the GCIPL or RNFL thickness of the remaining probe eye hemiretina.

Main outcome measures: Mean absolute error (MAE) and squared Pearson correlation coefficient (r²) were used to evaluate model performance.

Results: The deep learning model was able to predict superior and inferior GCIPL thicknesses with a global r² value of 0.90 and 0.86, r² of mean of 0.90 and 0.86, and mean MAE of 3.72 μm and 4.2 μm, respectively. For superior and inferior RNFL thickness predictions, model performance was slightly lower, with a global r² of 0.75 and 0.84, r² of mean of 0.81 and 0.82, and MAE of 9.31 μm and 8.57 μm, respectively. There was only a modest decrease in model performance when predicting GCIPL and RNFL in more severe disease. Using individualized hemiretinal predictions to account for variability across patients, we estimate that a clinical trial can detect a difference equivalent to a 25% treatment effect over 24 months with an 11-fold reduction in the number of patients compared to a conventional trial.

Conclusions: Our deep learning models were able to accurately estimate both macula GCIPL and ONH RNFL hemiretinal thickness. Using an internal control based on these model predictions may help reduce clinical trial sample size requirements and facilitate investigation of new glaucoma neuroprotection therapies.

Financial disclosure(s): Proprietary or commercial disclosure may be found after the references.

Keywords: Clinical trial; Deep learning; Glaucoma; Machine learning; Neuroprotection.