August 2019
Volume 60, Issue 11
Open Access
ARVO Imaging in the Eye Conference Abstract  |   August 2019
Early Stage Glaucoma Diagnosis by Artificial Intelligence Assisted Multifractal Functional OCT
Author Affiliations & Notes
  • Subrata Batabyal
    R&D, Nanoscope Technologies LLC, Bedford, Texas, United States
  • Sanghoon Kim
    R&D, Nanoscope Technologies LLC, Bedford, Texas, United States
  • Sourajit Mustafi
    R&D, Nanoscope Technologies LLC, Bedford, Texas, United States
  • Weldon Wright
    R&D, Nanoscope Technologies LLC, Bedford, Texas, United States
  • Samarendra Mohanty
    R&D, Nanoscope Technologies LLC, Bedford, Texas, United States
  • Footnotes
    Commercial Relationships   Subrata Batabyal, Nanoscope Technologies (P), Nanoscope Technologies (E); Sanghoon Kim, Nanoscope Technologies (E); Sourajit Mustafi, Nanoscope Technologies (E); Weldon Wright, Nanoscope Technologies (E); Samarendra Mohanty, Nanoscope Technologies (I), Nanoscope Technologies (P), Nanoscope Technologies (E)
  • Footnotes
    Support  None
Investigative Ophthalmology & Visual Science August 2019, Vol.60, PB0112. doi:https://doi.org/
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      Subrata Batabyal, Sanghoon Kim, Sourajit Mustafi, Weldon Wright, Samarendra Mohanty; Early Stage Glaucoma Diagnosis by Artificial Intelligence Assisted Multifractal Functional OCT. Invest. Ophthalmol. Vis. Sci. 2019;60(11):PB0112. doi: https://doi.org/.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : Glaucoma is a leading eye disorder which has few symptoms and difficult to detect in early stages. The detection of disease progression remains challenging in glaucoma due to the variable and slowly progressive nature of the disease, measurement-variability in standard assessment procedure and of imaging devices, and the lack of a commonly acceptable reference standard. Herein, we describe the development of multifractal OCT with added artificial intelligence (AI) to detect early signs of glaucoma from structural and functional imaging.

Methods : Superior nano/micro-structural alteration and movement associated with retinal/trabecular meshwork dysfunction is obtained by locally connected fractal dimension analysis of B and C-scan OCT image. The stimulation enabled fluctuations in retinal ganglion cell (RGC) layer and pulsatile motion of the trabecular meshwork (TM) is probed by multifractal analysis of the spatial and temporal-varying phase/intensity signal of OCT scan. Based on this method, we have developed a multifractal OCT device for optically detecting changes in RGC/TM activities for early diagnosis of glaucoma. Feature extraction from multifractal parameters is achieved by AI. A large set of multifractal images are used in training and weight calibration, which is validated with respect to ground truth.

Results : Our preliminary studies based on Multifractal OCT system could differentiate between wild type (control) and mouse with RGC dysfunction. Successful identification of glaucoma in mouse model was possible by analyzing the functional and structural OCT data using multifractal algorithm.

Conclusions : Taken together, the AI assisted multifractal OCT system will pave the way for a clinically translatable approach for early stage glaucoma detection. People at greater risk for developing glaucoma can greatly benefit with such early diagnostics for timely intervention.

This abstract was presented at the 2019 ARVO Imaging in the Eye Conference, held in Vancouver, Canada, April 26-27, 2019.

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