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RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure.

Publication ,  Journal Article
Datta, S; Mariottoni, EB; Dov, D; Jammal, AA; Carin, L; Medeiros, FA
Published in: Sci Rep
June 15, 2021

Glaucoma is the leading cause of irreversible blindness in the world, affecting over 70 million people. The cumbersome Standard Automated Perimetry (SAP) test is most frequently used to detect visual loss due to glaucoma. Due to the SAP test's innate difficulty and its high test-retest variability, we propose the RetiNerveNet, a deep convolutional recursive neural network for obtaining estimates of the SAP visual field. RetiNerveNet uses information from the more objective Spectral-Domain Optical Coherence Tomography (SDOCT). RetiNerveNet attempts to trace-back the arcuate convergence of the retinal nerve fibers, starting from the Retinal Nerve Fiber Layer (RNFL) thickness around the optic disc, to estimate individual age-corrected 24-2 SAP values. Recursive passes through the proposed network sequentially yield estimates of the visual locations progressively farther from the optic disc. While all the methods used for our experiments exhibit lower performance for the advanced disease group (possibly due to the "floor effect" for the SDOCT test), the proposed network is observed to be more accurate than all the baselines for estimating the individual visual field values. We further augment the proposed network to additionally predict the SAP Mean Deviation values and also facilitate the assignment of higher weightage to the underrepresented groups in the data. We then study the resulting performance trade-offs of the RetiNerveNet on the early, moderate and severe disease groups.

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Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

June 15, 2021

Volume

11

Issue

1

Start / End Page

12562

Location

England

Related Subject Headings

  • Visual Fields
  • Visual Field Tests
  • Tomography, Optical Coherence
  • Retinal Ganglion Cells
  • Retina
  • Optic Disk
  • Neural Networks, Computer
  • Nerve Fibers
  • Middle Aged
  • Male
 

Citation

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Datta, S., Mariottoni, E. B., Dov, D., Jammal, A. A., Carin, L., & Medeiros, F. A. (2021). RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure. Sci Rep, 11(1), 12562. https://doi.org/10.1038/s41598-021-91493-9
Datta, Shounak, Eduardo B. Mariottoni, David Dov, Alessandro A. Jammal, Lawrence Carin, and Felipe A. Medeiros. “RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure.Sci Rep 11, no. 1 (June 15, 2021): 12562. https://doi.org/10.1038/s41598-021-91493-9.
Datta S, Mariottoni EB, Dov D, Jammal AA, Carin L, Medeiros FA. RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure. Sci Rep. 2021 Jun 15;11(1):12562.
Datta, Shounak, et al. “RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure.Sci Rep, vol. 11, no. 1, June 2021, p. 12562. Pubmed, doi:10.1038/s41598-021-91493-9.
Datta S, Mariottoni EB, Dov D, Jammal AA, Carin L, Medeiros FA. RetiNerveNet: using recursive deep learning to estimate pointwise 24-2 visual field data based on retinal structure. Sci Rep. 2021 Jun 15;11(1):12562.

Published In

Sci Rep

DOI

EISSN

2045-2322

Publication Date

June 15, 2021

Volume

11

Issue

1

Start / End Page

12562

Location

England

Related Subject Headings

  • Visual Fields
  • Visual Field Tests
  • Tomography, Optical Coherence
  • Retinal Ganglion Cells
  • Retina
  • Optic Disk
  • Neural Networks, Computer
  • Nerve Fibers
  • Middle Aged
  • Male