June 2023
Volume 64, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2023
Deep Learning Estimation of 10-2 Visual Field Map Based on Macular Optical Coherence Tomography Angiography Measurements
Author Affiliations & Notes
  • Golnoush Mahmoudinezhad
    Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, La Jolla, California, United States
  • Sasan Moghimi
    Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, La Jolla, California, United States
  • Jiacheng Cheng
    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, United States
  • Liang Ru
    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, United States
  • Dongchen Yang
    Department of Computer Science and Engineering, University of California San Diego, La Jolla, California, United States
  • Siavash Beheshtaein
    L3Harris Technologies, Torrance, California, United States
  • Kelvin H Du
    Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, La Jolla, California, United States
  • Kareem Latif
    Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, La Jolla, California, United States
  • Gopikasree Gunasegaran
    Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, La Jolla, California, United States
  • Eleonora Micheletti
    Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, La Jolla, California, United States
  • Takashi Nishida
    Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, La Jolla, California, United States
  • Alireza Kamalipour
    Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, La Jolla, California, United States
  • Mark Christopher
    Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, La Jolla, California, United States
  • Linda M Zangwill
    Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, La Jolla, California, United States
  • Nuno Vasconcelos
    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, California, United States
  • Robert N Weinreb
    Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, La Jolla, California, United States
  • Footnotes
    Commercial Relationships   Golnoush Mahmoudinezhad None; Sasan Moghimi Tobacco-Related Disease Research Program T31IP1511, Code F (Financial Support), R01EY034148, Code F (Financial Support); Jiacheng Cheng None; Liang Ru None; Dongchen Yang None; Siavash Beheshtaein None; Kelvin H Du UCSD school of medicine summer research grant, Code F (Financial Support); Kareem Latif None; Gopikasree Gunasegaran None; Eleonora Micheletti None; Takashi Nishida Topcon, Code C (Consultant/Contractor); Alireza Kamalipour Fight for Sight, Code F (Financial Support); Mark Christopher K99EY030942, Code F (Financial Support), R01EY034146, Code F (Financial Support), AISight Health, Code P (Patent); Linda Zangwill Abbvie Inc. Topcon, Code C (Consultant/Contractor), EY11008 (Old DIGS), EY19869 (ADAGESII), EY14267 (ADAGESU10), EY027510 (DIGS Myopia) , EY026574 (ADAGESIV), P30EY022589 (bold is current from 2018), R21EY031125, R01EY034146 (Multimodal AI) BrightFocus Foundation (by the donors of the National Glaucoma Research Program, a program of the BrightFocus Foundation grant #2017122 ) The Glaucoma Foundation, Code F (Financial Support), F: National Eye Institute, Carl Zeiss Meditec Inc., Heidelberg Engineering GmbH, Optovue Inc., Topcon Medical Systems Inc, Code F (Financial Support), Zeiss Meditec, AiSight Health, Code P (Patent); Nuno Vasconcelos National Science Foundation (NSF) grants (IIS-1924937 and IIS-2041009)., Code F (Financial Support), Amazon, Nautilus cluster, Code F (Financial Support); Robert Weinreb Abbvie, Aerie Pharmaceuticals, Allergan, Equinox, Iantrek, Implandata, Nicox, Topcon Medical , Code C (Consultant/Contractor), EY023704 (ADAGESIII ), EY029058 (OCTA) Research to Prevent Blindness, Code F (Financial Support), Bausch & Lomb, Topcon Medical, Heidelberg Engineering, Carl Zeiss Meditec, Optovue, Centervue , Code F (Financial Support), Toromedes, Carl Zeiss Meditec, Code P (Patent)
  • Footnotes
    Support  EY11008, EY19869, EY14267, EY027510 , EY026574 , P30EY022589, R21EY031125, K99EY030942, EY034146, BrightFocus Foundation (by the donors of the National Glaucoma Research Program, a program of the BrightFocus Foundation grant #2017122 ), EY023704, EY029058, Research to Prevent Blindness, , Tobacco-Related Disease Research Program T31IP1511, R01EY034148, National Science Foundation (NSF) grants (IIS-1924937 and IIS-2041009).
Investigative Ophthalmology & Visual Science June 2023, Vol.64, 2022. doi:
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      Golnoush Mahmoudinezhad, Sasan Moghimi, Jiacheng Cheng, Liang Ru, Dongchen Yang, Siavash Beheshtaein, Kelvin H Du, Kareem Latif, Gopikasree Gunasegaran, Eleonora Micheletti, Takashi Nishida, Alireza Kamalipour, Mark Christopher, Linda M Zangwill, Nuno Vasconcelos, Robert N Weinreb; Deep Learning Estimation of 10-2 Visual Field Map Based on Macular Optical Coherence Tomography Angiography Measurements. Invest. Ophthalmol. Vis. Sci. 2023;64(8):2022.

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

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Abstract

Purpose : To develop deep learning (DL) models estimating central visual field (VF) from optical coherence tomography angiography (OCTA) vessel density measurements.

Methods : This study included 3405 macula OCTA (1872 DIGS (Diagnostic Innovations in Glaucoma Study), and 1533 VO (Viterbi Ophthalmology )) from 1176 participants (1950 eyes). Data was split on the patient level into training (80%), validation (10%), and test sets (10%). DL ResNet50 models were trained on enface macula vessel density OCTA images to estimate 10-2 mean deviation (MD), pattern standard deviation (PSD), 68 total deviation (TD), and pattern deviation (PD) values and compared with linear regression (LR) model with the same input. Accuracy of the models was evaluated by calculating the average mean absolute error (MAE) and the R (Pearson correlation coefficients) of the estimated and actual VF values.

Results : DL models predicting 10-2 MD achieved R of 0.92 (95% confidence interval [CI], 86–0.96) for 10-2 MD and 0.94 (95% confidence interval [CI], 90–0.97) for 10-2 PSD and MAEs of 1.76 dB (95% CI, 1.39–2.17 dB) for MD and 0.79 dB (95% CI, 0.55–1.05 dB) for PSD. This was significantly better than mean linear estimates for 10-2 MD ([R: 0.58 95% CI, 0.42–0.71] and [MAE: 3.36 dB 95% CI, 2.58–4.22 dB]) and 10-2 PSD ([R: 0.54 95% CI, 0.37–0.68] and [MAE: 2.57 dB 95% CI, 2.05–3.10 dB]). The DL model outperformed the LR model for the estimation of pointwise TD values with an average MAE of 2.48 dB (95% CI: 1.99, 3.02) vs. 4.28 dB (95% CI: 3.46, 5.15) and R of 0.83 (95% CI: 0.76, 0.87) vs. 0.40 (95% CI: 0.26, 0.52) over all test points (Figure1). Similarly, the DL model outperformed the LR model for the estimation of pointwise PD values (Figure2). The DL model outperformed the LR model for the estimation of all sectors in 10-2 VF. Sector predictions ranged from MAE of 1.45(1.16, 1.77) dB (R=0.91 (0.80, 0.96) for inferotemporal sector to MAE of 2.99(2.21, 3.78) dB (R= 0.86 (0.76, 0.94) in superiornasal sector of VF.

Conclusions : DL models estimates functional loss from OCTA enface scans with high accuracy. Applying DL to the OCTA images may possibly lead to new biomarkers for clinical decision making and may lead to improved individualized patient care and risk stratification of patients who are at risk for central VF damage.

This abstract was presented at the 2023 ARVO Annual Meeting, held in New Orleans, LA, April 23-27, 2023.

 

 

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