Pointwise Visual Field Estimation From Optical Coherence Tomography in Glaucoma Using Deep Learning

Abstract

Purpose: Standard automated perimetry is the gold standard to monitor visual field (VF) loss in glaucoma management, but it is prone to intrasubject variability. We trained and validated a customized deep learning (DL) regression model with Xception backbone that estimates pointwise and overall VF sensitivity from unsegmented optical coherence tomography (OCT) scans.

Methods: DL regression models have been trained with four imaging modalities (circumpapillary OCT at 3.5 mm, 4.1 mm, and 4.7 mm diameter) and scanning laser ophthalmoscopy en face images to estimate mean deviation (MD) and 52 threshold values. This retrospective study used data from patients who underwent a complete glaucoma examination, including a reliable Humphrey Field Analyzer (HFA) 24-2 SITA Standard (SS) VF exam and a SPECTRALIS OCT.

Results: For MD estimation, weighted prediction averaging of all four individuals yielded a mean absolute error (MAE) of 2.89 dB (2.50-3.30) on 186 test images, reducing the baseline by 54% (MAEdecr%). For 52 VF threshold values' estimation, the weighted ensemble model resulted in an MAE of 4.82 dB (4.45-5.22), representing an MAEdecr% of 38% from baseline when predicting the pointwise mean value. DL managed to explain 75% and 58% of the variance (R2) in MD and pointwise sensitivity estimation, respectively.

Conclusions: Deep learning can estimate global and pointwise VF sensitivities that fall almost entirely within the 90% test-retest confidence intervals of the 24-2 SS test.

Translational relevance: Fast and consistent VF prediction from unsegmented OCT scans could become a solution for visual function estimation in patients unable to perform reliable VF exams.

Figure 1.

T op panel: Overview of imaging modalities, the spatial relationship between structure and analyzed VF points, allocated to Garway-Heath sectors. The 30° SLO covers less than half of VF test locations, still considerably more than the three circumpapillary OCT scans (white circles) displayed on the right. Bottom panel: Four cases of the independent test set. Each case features (1) an ONH zoom of the original 30° SLO image, (2) measured VF map and MD, and (3) the corresponding predicted VF map and MD. The displayed cases include an example of early glaucoma (top left), moderate glaucoma with loss in the superior hemifield (bottom left), a myopic eye with severe glaucomatous loss in the inferior hemifield (top right), and severe glaucoma with only a small central island remaining (bottom right).

Figure 2.

(A) MAEdecr% values for 52 VF threshold values obtained using the model trained on 4.7 mm (outer) OCT scans. MAEdecr% is the decrease in percentage from the baseline MAE, with the latter obtained when always predicting the pointwise mean. (B) Similar to panel A, but model trained using en face SLO images, to compare with as a baseline. (C) Final MAEdecr% values obtained on the test set, using the weighted averaged predictions of the four CNNs trained using OCT scans and SLO images. (D) The difference between panels A and B, indicating the superior VF modeling performance of OCT scans across the majority of VF test locations.

Figure 3.

Comparative overview of three original studies (current, Guo et al., and Zhu et al.¹²) that report on the relationship between measured and predicted VF threshold values, stratified by sensitivity (step size of 2 dB). The error ranges obtained by our approach leveraging DL are smaller than previous non-DL studies. Thirty-three of 38 whiskers are located within the 90% CI test–retest limits reported by Artes et al.