Investigating Determinants and Evaluating Deep Learning Training Approaches for Visual Acuity in Foveal Hypoplasia

Volha V Malechka; Dat Duong; Keyla D Bordonada; Amy Turriff; Delphine Blain; Elizabeth Murphy; Wendy J Introne; Bernadette R Gochuico; David R Adams; Wadih M Zein; Brian P Brooks; Laryssa A Huryn; Benjamin D Solomon; Robert B Hufnagel

doi:10.1016/j.xops.2022.100225

Investigating Determinants and Evaluating Deep Learning Training Approaches for Visual Acuity in Foveal Hypoplasia

Ophthalmol Sci. 2022 Sep 24;3(1):100225. doi: 10.1016/j.xops.2022.100225. eCollection 2023 Mar.

Authors

Volha V Malechka¹, Dat Duong², Keyla D Bordonada¹, Amy Turriff¹, Delphine Blain¹, Elizabeth Murphy¹, Wendy J Introne^{2

3}, Bernadette R Gochuico^{2

3}, David R Adams^{2

3}, Wadih M Zein¹, Brian P Brooks¹, Laryssa A Huryn¹, Benjamin D Solomon², Robert B Hufnagel¹

Affiliations

¹ Ophthalmic Genetics and Visual Function Branch, National Eye Institute, National Institutes of Health, Bethesda, Maryland.
² Medical Genomics Unit, Medical Genetics Branch, National Human Genome Research Institute, Bethesda, Maryland.
³ Human Biochemical Genetics Section, National Human Genome Research Institute, Bethesda, Maryland.

Abstract

Purpose: To describe the relationships between foveal structure and visual function in a cohort of individuals with foveal hypoplasia (FH) and to estimate FH grade and visual acuity using a deep learning classifier.

Design: Retrospective cohort study and experimental study.

Participants: A total of 201 patients with FH were evaluated at the National Eye Institute from 2004 to 2018.

Methods: Structural components of foveal OCT scans and corresponding clinical data were analyzed to assess their contributions to visual acuity. To automate FH scoring and visual acuity correlations, we evaluated the following 3 inputs for training a neural network predictor: (1) OCT scans, (2) OCT scans and metadata, and (3) real OCT scans and fake OCT scans created from a generative adversarial network.

Main outcome measures: The relationships between visual acuity outcomes and determinants, such as foveal morphology, nystagmus, and refractive error.

Results: The mean subject age was 24.4 years (range, 1-73 years; standard deviation = 18.25 years) at the time of OCT imaging. The mean best-corrected visual acuity (n = 398 eyes) was equivalent to a logarithm of the minimal angle of resolution (LogMAR) value of 0.75 (Snellen 20/115). Spherical equivalent refractive error (SER) ranged from -20.25 diopters (D) to +13.63 D with a median of +0.50 D. The presence of nystagmus and a high-LogMAR value showed a statistically significant relationship (P < 0.0001). The participants whose SER values were farther from plano demonstrated higher LogMAR values (n = 382 eyes). The proportion of patients with nystagmus increased with a higher FH grade. Variability in SER with grade 4 (range, -20.25 D to +13.00 D) compared with grade 1 (range, -8.88 D to +8.50 D) was statistically significant (P < 0.0001). Our neural network predictors reliably estimated the FH grading and visual acuity (correlation to true value > 0.85 and > 0.70, respectively) for a test cohort of 37 individuals (98 OCT scans). Training the predictor on real OCT scans with metadata and fake OCT scans improved the accuracy over the model trained on real OCT scans alone.

Conclusions: Nystagmus and foveal anatomy impact visual outcomes in patients with FH, and computational algorithms reliably estimate FH grading and visual acuity.

Keywords: BCVA, best-corrected visual acuity; CHS, Chediak–Higashi syndrome; D, diopters; FH, foveal hypoplasia; Foveal hypoplasia; GAN, generative adversarial network; Generative adversarial network; HPS, Hermansky–Pudlak syndrome; LogMAR, logarithm of the minimal angle of resolution; NEI, National Eye Institute; Neural network classifier; Nystagmus; OCT; PAX6, Paired Box 6 gene; SER, spherical equivalent refractive error; WAGR, Wilms tumor-aniridia-genital anomalies-retardation syndrome.