June 2020
Volume 61, Issue 7
Free
ARVO Annual Meeting Abstract  |   June 2020
From Machine to the Real World: Assessing the Accuracy of a Machine-to-Machine (M2M) Deep Learning Model to Detect Glaucoma during a Population-Based Screening Effort in Brazil
Author Affiliations & Notes
  • Tais Estrela
    Duke University, Durham, North Carolina, United States
  • Eduardo Bicalho Mariottoni
    Duke University, Durham, North Carolina, United States
  • Tabatta Graciolli
    Ophthalmology, University of State of Rio de Janeiro, Rio de Janeiro, Brazil
  • Nubia Chouchounova
    Ophthalmology, Hospital Evangélico of Belo Horizonte, Brazil
  • Nara Ogata
    Ophthalmology, University of Sao Paulo, Sao Paulo, Sao Paulo, Brazil
  • Mariana Siqueira
    Ophthalmology, Federal University of São Paulo, Brazil
  • Carla Urata
    Ophthalmology, University of Sao Paulo, Sao Paulo, Sao Paulo, Brazil
  • Leonardo Shigueoka
    Duke University, Durham, North Carolina, United States
  • Alessandro A Jammal
    Duke University, Durham, North Carolina, United States
  • Atalie C. Thompson
    Duke University, Durham, North Carolina, United States
  • Felipe A Medeiros
    Duke University, Durham, North Carolina, United States
  • Footnotes
    Commercial Relationships   Tais Estrela, None; Eduardo Mariottoni, None; Tabatta Graciolli, None; Nubia Chouchounova, None; Nara Ogata, None; Mariana Siqueira, None; Carla Urata, None; Leonardo Shigueoka, None; Alessandro Jammal, None; Atalie Thompson, None; Felipe Medeiros, Aeri Pharmaceuticals (C), Allergan (C), Annexon (C), Biogen (C), Biozeus (C), Carl-Zeiss Meditec (F), Carl-Zeiss Meditec (C), Galimedix (C), Google (F), Heidelberg Engineering (F), IDx (C), NGoogle (P), Novartis (C), Reichert (F), Reichert (C), Stealth Biotherapeutics (C)
  • Footnotes
    Support  NEI EY029885 (FAM)
Investigative Ophthalmology & Visual Science June 2020, Vol.61, 4539. doi:
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      Tais Estrela, Eduardo Bicalho Mariottoni, Tabatta Graciolli, Nubia Chouchounova, Nara Ogata, Mariana Siqueira, Carla Urata, Leonardo Shigueoka, Alessandro A Jammal, Atalie C. Thompson, Felipe A Medeiros; From Machine to the Real World: Assessing the Accuracy of a Machine-to-Machine (M2M) Deep Learning Model to Detect Glaucoma during a Population-Based Screening Effort in Brazil. Invest. Ophthalmol. Vis. Sci. 2020;61(7):4539.

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

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Abstract

Purpose : To evaluate the ability of a deep learning model trained to objectively quantify neural loss in fundus photographs to detect patients with glaucoma during ophthalmic screening in Brazil.

Methods : A deep convolutional neural network was trained to objectively predict retinal nerve fiber layer thickness from fundus photographs using 66,555 pairs of optical coherence tomography and photos from 10,144 eyes of 6,039 subjects. Data for training and validation of the algorithm was extracted from the Duke Glaucoma Registry. The trained Machine-to-Machine (M2M) deep learning model was then subsequently used to objectively detect glaucomatous damage on the fundus photographs of 3347 eyes of 1,990 subjects examined as part of population-based screening projects in 3 cities of Brazil (Bahia, Curitiba and Rio de Janeiro). Fundus photographs during the screening projects were obtained with a smartphone-based fundus camera (Phelcom Eyer, Phelcom). Photographs were also subjectively assessed by masked graders.

Results : The trained M2M model predictions had high correlation with the actual RNFL thickness measurements on the DGR dataset (r = 0.81; P<0.001). When tested in the population-based screening dataset, the M2M model was able to significantly discriminate eyes detected as glaucomatous versus normal, if graders were used as the reference standard (P<0.001).

Conclusions : A deep learning model trained to quantify neural damage on fundus photographs was able to objectively detect glaucomatous damage on data from a real-world screening project.

This is a 2020 ARVO Annual Meeting abstract.

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