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Pham AT, Bradley C, Hou K, Herbert P, Yohannan J. Detecting glaucoma worsening using optical coherence tomography derived visual field estimates. Sci Rep 2025; 15:5013. [PMID: 39929861 PMCID: PMC11811138 DOI: 10.1038/s41598-025-86217-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2024] [Accepted: 01/09/2025] [Indexed: 02/13/2025] Open
Abstract
Multiple glaucoma studies have attempted to generate visual field (VF) mean deviation (MD) estimates using cross-sectional optical coherence tomography (OCT) data. However, whether such models offer any value in detecting longitudinal VF progression is unclear. We address this by developing a machine learning (ML) model to convert OCT data to MD and assessing its ability to detect longitudinal worsening. In this study, we created a model dataset of 70,575 paired OCT/VFs to train an ML model to convert OCT to VF-MD. We created a separate progression dataset of 4,044 eyes with ≥ 5 paired OCT/VFs to assess the ability of OCT-derived MD to detect worsening. The progression dataset eyes had 2 additional unpaired VFs (≥ 7 total) to establish a "ground truth" rate of progression defined by MD slope. We used the ML model to generate longitudinal OCT-MD estimates for each OCT scan for progression dataset eyes. We calculated MD slopes after substituting/supplementing VF-MD with OCT-MD and measured the ability to detect progression. We labeled true progressors using a ground truth MD slope < 0.5 dB/year calculated from ≥ 7 VF-MD measurements. We compared the area under the curve (AUC) of MD slopes calculated using both VF-MD (with < 7 measurements) and OCT-MD. Because we found OCT-MD substitution had a statistically inferior AUC to VF-MD, we simulated the effect of reducing OCT-MD mean absolute error (MAE) on the ability to detect worsening. Our model's OCT-MD estimates had an MAE of 1.62 dB (better than that of any previously published models). However, we found the AUC of MD slopes with partial OCT-MD substitution was significantly worse than the VF-MD slope. Supplementing VF-MD with OCT-MD also did not improve AUC, regardless of MAE. We found that OCT-MD estimates needed an MAE ≤ 1.00 dB before AUC was statistically similar to VF-MD alone. Overall, our ML model converting OCT data to VF-MD had error levels lower than those published in prior work and was inferior to VF-MD data for detecting trend-based VF progression. Our data suggest that future models converting OCT data to VF-MD must achieve better prediction errors (MAE ≤ 1 dB) to be clinically valuable at detecting VF worsening.
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Affiliation(s)
- Alex T Pham
- University of Maryland School of Medicine, Baltimore, MD, USA
| | - Chris Bradley
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Kaihua Hou
- University of California San Francisco, San Francisco, CA, USA
| | - Patrick Herbert
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
| | - Jithin Yohannan
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
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Sun H, Guo R, Feng Q, Zhang X, Li K, Zheng N, He L, Liu S. Visualizing dynamic alterations of vitreous viscosity during elevated intraocular pressure in glaucoma with a Near-infrared/Magnetic resonance imaging dual-modal nanoprobe. J Colloid Interface Sci 2025; 679:529-538. [PMID: 39467364 DOI: 10.1016/j.jcis.2024.10.110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2024] [Revised: 09/19/2024] [Accepted: 10/18/2024] [Indexed: 10/30/2024]
Abstract
Glaucoma is a chronic progressive disease leading to irreversible visual impairment and blindness. High intraocular pressure (IOP) resulting from abnormally high outflow resistance is a major risk factor for glaucoma development, however, it is unclear how IOP elevation influences the structure and function of the retina and the optic nerve via vitreous humor located between the lens and retina in the eye. To understand vitreous biomechanical and stimulus response toward IOP elevation, we developed a novel near-infrared (NIR)/MRI dual-modal nanoprobe, DTA/P-NCA/17F@Co, which is composed of N, N-dimethyl-4(thien-2-yl)-aniline group (DTA) as NIR fluorophore and the fluorine-based polyamino acid cobalt nanoparticles (P-NCA/17F@Co) as T2 contrast agent. These nanoprobes exhibit good biocompatibility, low surface energy characteristics, and viscosity-responsive NIR emission and T2 relaxation values. The intrinsic viscosity-sensitivemechanismof nanoprobes was ascribed to constrained molecular motion in high-viscosity vitreous chamber, which causes enhanced fluorescence emission and shortened T2 relaxation times. By using its ability for dual-modal visualization of viscosity, we achieved non-invasive in vivo monitoring the changes in vitreous viscosity during elevated IOP in a glaucoma rat model. In vivo experiments validated that vitreous viscosity is very strongly correlated with IOP elevation induced by glaucoma, much earlier than structural and functional change in the retina. Our findings revealed that IOP elevation induced the increase of vitreous viscosity, indicating that monitoring vitreous viscosity is key to the glaucoma model. This study not only provides versatile nanoprobes for dual-modal visualization of biomechanical properties of the vitreous humor in its native environment, but also shows great potential in the early diagnosis of glaucoma.
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Affiliation(s)
- Hao Sun
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China; Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou, Henan, 450000, China; Key Laboratory of Microsystems and Microstructures Manufacturing (Ministry of Education), Harbin Institute of Technology, Harbin 150001, China
| | - Ruiqi Guo
- Key Laboratory of Microsystems and Microstructures Manufacturing (Ministry of Education), Harbin Institute of Technology, Harbin 150001, China; School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China
| | - Qingying Feng
- Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou, Henan, 450000, China; Key Laboratory of Microsystems and Microstructures Manufacturing (Ministry of Education), Harbin Institute of Technology, Harbin 150001, China; School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China
| | - Xue Zhang
- Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou, Henan, 450000, China; Key Laboratory of Microsystems and Microstructures Manufacturing (Ministry of Education), Harbin Institute of Technology, Harbin 150001, China; School of Life Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China
| | - Kai Li
- Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou, Henan, 450000, China; Key Laboratory of Microsystems and Microstructures Manufacturing (Ministry of Education), Harbin Institute of Technology, Harbin 150001, China; School of Medicine and Health, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China
| | - Nannan Zheng
- Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou, Henan, 450000, China; Key Laboratory of Microsystems and Microstructures Manufacturing (Ministry of Education), Harbin Institute of Technology, Harbin 150001, China; School of Medicine and Health, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China
| | - Liangcan He
- Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou, Henan, 450000, China; Key Laboratory of Microsystems and Microstructures Manufacturing (Ministry of Education), Harbin Institute of Technology, Harbin 150001, China; School of Medicine and Health, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.
| | - Shaoqin Liu
- School of Chemistry and Chemical Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China; Zhengzhou Research Institute, Harbin Institute of Technology, Zhengzhou, Henan, 450000, China; Key Laboratory of Microsystems and Microstructures Manufacturing (Ministry of Education), Harbin Institute of Technology, Harbin 150001, China; School of Medicine and Health, Harbin Institute of Technology, Harbin, Heilongjiang, 150001, China.
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Akmaz O, Tokac MG, Garli M, Kusbeci T. Comparison of glaucoma progression rate in glaucoma patients at different stages using guided progression analysis with optical coherence tomography. BMC Ophthalmol 2025; 25:1. [PMID: 39748393 PMCID: PMC11694462 DOI: 10.1186/s12886-024-03837-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2024] [Accepted: 12/27/2024] [Indexed: 01/04/2025] Open
Abstract
BACKGROUND The aim of the present study was to compare the rates of change in Ganglion Cell- Inner Plexiform Layer (GCIPL) and Retinal Nerve Fiber Layer (RNFL) thickness, as measured by Optical Coherence Tomography (OCT) Guided Progression Analysis (GPA) program in control group, Primary Open Angle Glaucoma (POAG) and Pseudoexfoliation Glaucoma (PXG) eyes. METHODS 60 POAG and 60 PXG patients and 30 control group patients were included in the study. Patients diagnosed with glaucoma were divided into two groups as mild (Mean deviation (MD) > -6.00) and moderate-severe (MD < -6.00). The average, superior and inferior quadrant thinning rates (expressed in micrometers per year) of GCIPL and RNFL in the OCT GPA program were compared between groups. RESULTS Average GCIPL thinning rates were -0.23 ± 0.21 μm/year in the control group, -0.64 ± 0.54 μm/year in POAG patients, and -1.06 ± 1.16 μm/year in PXG patients (ANOVA, p < 0.001). Average RNFL thinning rates were -0.33 ± 0.44 μm/year in the control group, -0.86 ± 0.73 μm/year in POAG patients, and -1.33 ± 1.4 μm/year in PXG patients (ANOVA, p < 0.001). CONCLUSIONS The rates of GCIPL and RNFL thinning were highest in patients with PXG. We found that the glaucoma stage did not affect the rate of RNFL and GCIPL thinning.
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Affiliation(s)
- Okan Akmaz
- Department of Ophthalmology, University of Health Sciences, Izmir Bozyaka Education and Research Hospital, Bahar Mah. Saim Çıkrıkcı Cad No: 59, Karabağlar, Turkey.
| | - Murat Gokhan Tokac
- Department of Ophthalmology, University of Health Sciences, Izmir Bozyaka Education and Research Hospital, Bahar Mah. Saim Çıkrıkcı Cad No: 59, Karabağlar, Turkey
| | - Murat Garli
- Department of Ophthalmology, University of Health Sciences, Izmir Bozyaka Education and Research Hospital, Bahar Mah. Saim Çıkrıkcı Cad No: 59, Karabağlar, Turkey
| | - Tuncay Kusbeci
- Department of Ophthalmology, University of Health Sciences, Izmir Bozyaka Education and Research Hospital, Bahar Mah. Saim Çıkrıkcı Cad No: 59, Karabağlar, Turkey
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Wu JH, Moghimi S, Walker E, Nishida T, Liebmann JM, Fazio MA, Girkin CA, Zangwill LM, Weinreb RN. Time to Glaucoma Progression Detection by Optical Coherence Tomography and Visual Field in Glaucoma Individuals of African Descent. Am J Ophthalmol 2025; 269:195-204. [PMID: 39094992 DOI: 10.1016/j.ajo.2024.07.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Revised: 06/14/2024] [Accepted: 07/23/2024] [Indexed: 08/04/2024]
Abstract
PURPOSE To examine the time to glaucoma progression detection by retinal nerve fiber layer thickness (RNFLT) and visual field (VF) among individuals of African descent (AD). DESIGN Retrospective cohort study. METHODS This multicenter study included eyes with glaucoma from individuals of AD from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study with ≥2 years/5 visits of optic nerve head RNFLT and 24-2 VF examinations. INTERVENTION OR OBSERVATION PROCEDURE Rates of VF mean deviation (MD) and RNFLT worsening were analyzed using linear mixed-effects models, and longitudinal data were simulated using the variability estimates. MAIN OUTCOME MEASURE The simulated time to detect trend-based glaucoma progression was assessed with assumed rates of VF MD and RNFLT change derived from the cohort (25th, 50th, and 75th percentile [as p25, median, and p75] slopes and mean slopes). Severity-stratified analyses were also performed. RESULTS We included 184 eyes from 128 subjects of AD (mean baseline age 63.4 years; VF MD -4.2 dB; RNFLT 80.2 µm). The p25, median, mean, and p75 rates of change were -0.43, -1.01, -1.15, and -1.64 µm per year for RNFLT, and 0.00, -0.21, -0.30, and -0.51 dB per year for VF MD, respectively. Compared with VF MD, RNFLT showed an overall shorter mean time to progression detection (time difference 0.4-1.7 years), with the mean rates showing the largest difference (RNFLT 5.2 years vs VF MD 6.9 years). Similarly, we found an overall shorter time to detect RNFLT progression, compared with that of VF MD progression, in eyes with mild glaucoma (≥1 year earlier) and in eyes with moderate to advanced glaucoma (∼0.5 year earlier). CONCLUSIONS Computer simulation showed a potentially shorter time to detect RNFLT progression than VF MD progression in eyes from individuals of AD. Our findings support the importance of using RNFLT to detect progressive glaucoma in individuals of AD.
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Affiliation(s)
- Jo-Hsuan Wu
- From the Hamilton Glaucoma Center (J-H.W., S.M., E.W., T.N., L.M.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Sasan Moghimi
- From the Hamilton Glaucoma Center (J-H.W., S.M., E.W., T.N., L.M.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Evan Walker
- From the Hamilton Glaucoma Center (J-H.W., S.M., E.W., T.N., L.M.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Takashi Nishida
- From the Hamilton Glaucoma Center (J-H.W., S.M., E.W., T.N., L.M.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Jeffrey M Liebmann
- Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.), Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York, USA
| | - Massimo A Fazio
- Department of Ophthalmology and Vision Sciences (M.F., C.A.G.), Heersink School of Medicine, University of Alabama-Birmingham, Birmingham, Alabama, USA
| | - Christopher A Girkin
- Department of Ophthalmology and Vision Sciences (M.F., C.A.G.), Heersink School of Medicine, University of Alabama-Birmingham, Birmingham, Alabama, USA
| | - Linda M Zangwill
- From the Hamilton Glaucoma Center (J-H.W., S.M., E.W., T.N., L.M.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Robert N Weinreb
- From the Hamilton Glaucoma Center (J-H.W., S.M., E.W., T.N., L.M.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.
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Mohammadi M, Su E, Mohammadzadeh V, Besharati S, Martinyan A, Coleman AL, Law SK, Caprioli J, Weiss RE, Nouri-Mahdavi K. Comparison of Retinal Nerve Fiber Layer and Ganglion Cell Complex Rates of Change in Patients With Moderate to Advanced Glaucoma. Am J Ophthalmol 2024; 268:190-198. [PMID: 39111519 DOI: 10.1016/j.ajo.2024.07.025] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2024] [Revised: 07/16/2024] [Accepted: 07/24/2024] [Indexed: 09/01/2024]
Abstract
PURPOSE To compare ganglion cell complex (GCC) and retinal nerve fiber layer (RNFL) rates of change (RoC) in eyes with central or moderate to advanced glaucoma. DESIGN Prospective cohort study. PARTICIPANTS A total of 918 matched macular and RNFL OCT scan pairs from 109 eyes (109 patients) enrolled in the Advanced Glaucoma Progression Study with ≥2 years of follow-up and ≥4 OCT scans. METHODS We exported GCC and RNFL thickness measurements in 49 central macular superpixels and 12 RNFL clock-hour sectors, respectively. We applied our latest Bayesian hierarchical longitudinal model to estimate population and subject-specific baseline thickness (intercepts) and rates of change (RoC) in macular superpixels and RNFL sectors. Global RNFL and GCC RoC were analyzed in a single bivariate longitudinal model to properly compare them accounting for the correlation between their RoC. MAIN OUTCOME MEASURES Proportion of significant negative (deteriorating) and positive (improving) RoC expressed in μm/year. Standardized RoC were calculated by dividing RoC by the corresponding population SD. Analyses were repeated in eyes with visual field mean deviation (MD) ≤-6 and > -6 dB. RESULTS Average (SD) 24-2 visual field MD and follow-up length were -8.6 (6.3) dB and 4.2 (0.5) years, respectively. Global RNFL RoC (-0.70 µm/year) were faster than GCC (-0.44 µm/year) (P < .001); corresponding normalized RoC were not significantly different (P = .052). In bivariate analysis, patients with a significant negative global RNFL RoC (n = 63, 57%) or GCC (n = 56, 51%) frequently did so for both outcomes (n = 49, 45%). The average proportion of significantly decreasing RNFL sectors within an eye was 30.7% in eyes with MD > -6 dB compared to 20.5% in those with MD ≤ -6 dB (P = .014); the proportions for GCC superpixels were 21.1% versus 18.7%, respectively (P = .63). CONCLUSIONS Both GCC and RNFL measures can detect structural progression in glaucoma patients with central damage or moderate to advanced glaucoma. The clinical utility of RNFL imaging decreases with worsening severity of glaucoma.
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Affiliation(s)
- Massood Mohammadi
- Glaucoma Division, David Geffen School of Medicine, Stein Eye Institute, University of California Los Angeles (M.M., E.S., V.M., S.B., A.M., A.L.C., S.K.L., J.C., and K.N.-M.), Los Angeles, California, USA
| | - Erica Su
- Glaucoma Division, David Geffen School of Medicine, Stein Eye Institute, University of California Los Angeles (M.M., E.S., V.M., S.B., A.M., A.L.C., S.K.L., J.C., and K.N.-M.), Los Angeles, California, USA
| | - Vahid Mohammadzadeh
- Glaucoma Division, David Geffen School of Medicine, Stein Eye Institute, University of California Los Angeles (M.M., E.S., V.M., S.B., A.M., A.L.C., S.K.L., J.C., and K.N.-M.), Los Angeles, California, USA
| | - Sajad Besharati
- Glaucoma Division, David Geffen School of Medicine, Stein Eye Institute, University of California Los Angeles (M.M., E.S., V.M., S.B., A.M., A.L.C., S.K.L., J.C., and K.N.-M.), Los Angeles, California, USA
| | - Arthur Martinyan
- Glaucoma Division, David Geffen School of Medicine, Stein Eye Institute, University of California Los Angeles (M.M., E.S., V.M., S.B., A.M., A.L.C., S.K.L., J.C., and K.N.-M.), Los Angeles, California, USA
| | - Anne L Coleman
- Glaucoma Division, David Geffen School of Medicine, Stein Eye Institute, University of California Los Angeles (M.M., E.S., V.M., S.B., A.M., A.L.C., S.K.L., J.C., and K.N.-M.), Los Angeles, California, USA
| | - Simon K Law
- Glaucoma Division, David Geffen School of Medicine, Stein Eye Institute, University of California Los Angeles (M.M., E.S., V.M., S.B., A.M., A.L.C., S.K.L., J.C., and K.N.-M.), Los Angeles, California, USA
| | - Joseph Caprioli
- Glaucoma Division, David Geffen School of Medicine, Stein Eye Institute, University of California Los Angeles (M.M., E.S., V.M., S.B., A.M., A.L.C., S.K.L., J.C., and K.N.-M.), Los Angeles, California, USA
| | - Robert E Weiss
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles (R.E.W.), Los Angeles, California, USA
| | - Kouros Nouri-Mahdavi
- Glaucoma Division, David Geffen School of Medicine, Stein Eye Institute, University of California Los Angeles (M.M., E.S., V.M., S.B., A.M., A.L.C., S.K.L., J.C., and K.N.-M.), Los Angeles, California, USA.
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Pham AT, Bradley C, Hou K, Herbert P, Unberath M, Ramulu PY, Yohannan J. Detecting Glaucoma Worsening Using Optical Coherence Tomography Derived Visual Field Estimates. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.10.17.24315710. [PMID: 39484252 PMCID: PMC11527071 DOI: 10.1101/2024.10.17.24315710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/03/2024]
Abstract
Objective Multiple studies have attempted to generate visual field (VF) mean deviation (MD) estimates using cross-sectional optical coherence tomography (OCT) data. However, whether such models offer any value in detecting longitudinal VF progression is unclear. We address this by developing a machine learning (ML) model to convert OCT data to MD and assessing its ability to detect longitudinal worsening. Design Retrospective, longitudinal study. Participants A model dataset of 70,575 paired OCT/VFs to train an ML model converting OCT to VF-MD. A separate progression dataset of 4,044 eyes with ≥ 5 paired OCT/VFs to assess the ability of OCT-derived MD to detect worsening. Progression dataset eyes had two additional unpaired VFs (≥ 7 total) to establish a "ground truth" rate of progression defined by MD slope. Methods We trained an ML model using paired VF/OCT data to estimate MD measurements for each OCT scan (OCT-MD). We used this ML model to generate longitudinal OCT-MD estimates for progression dataset eyes. We calculated MD slopes after substituting/supplementing VF-MD with OCT-MD and measured the ability to detect progression. We labeled true progressors using a ground truth MD slope <0.5 dB/year calculated from ≥ 7 VF-MD measurements. We compared the area under the curve (AUC) of MD slopes calculated using both VF-MD (with <7 measurements) and OCT-MD. Because we found OCT-MD substitution had a statistically inferior AUC to VF-MD, we simulated the effect of reducing OCT-MD mean absolute error (MAE) on the ability to detect worsening. Main Outcome Measures AUC. Results OCT-MD estimates had an MAE of 1.62 dB. AUC of MD slopes with partial OCT-MD substitution was significantly worse than the VF-MD slope. Supplementing VF-MD with OCT-MD also did not improve AUC, regardless of MAE. OCT-MD estimates needed an MAE ≤ 1.00 dB before AUC was statistically similar to VF-MD alone. Conclusion ML models converting OCT data to VF-MD with error levels lower than published in prior work (MAE: 1.62 dB) were inferior to VF-MD data for detecting trend-based VF progression. Models converting OCT data to VF-MD must achieve better prediction errors (MAE ≤ 1 dB) to be clinically valuable at detecting VF worsening.
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Chen Y, Zhang X, Yang J, Han G, Zhang H, Lai M, Zhao J. HDB-Net: hierarchical dual-branch network for retinal layer segmentation in diseased OCT images. BIOMEDICAL OPTICS EXPRESS 2024; 15:5359-5383. [PMID: 39296382 PMCID: PMC11407236 DOI: 10.1364/boe.530469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 07/12/2024] [Accepted: 07/29/2024] [Indexed: 09/21/2024]
Abstract
Optical coherence tomography (OCT) retinal layer segmentation is a critical procedure of the modern ophthalmic process, which can be used for diagnosis and treatment of diseases such as diabetic macular edema (DME) and multiple sclerosis (MS). Due to the difficulties of low OCT image quality, highly similar retinal interlayer morphology, and the uncertain presence, shape and size of lesions, the existing algorithms do not perform well. In this work, we design an HDB-Net network for retinal layer segmentation in diseased OCT images, which solves this problem by combining global and detailed features. First, the proposed network uses a Swin transformer and Res50 as a parallel backbone network, combined with the pyramid structure in UperNet, to extract global context and aggregate multi-scale information from images. Secondly, a feature aggregation module (FAM) is designed to extract global context information from the Swin transformer and local feature information from ResNet by introducing mixed attention mechanism. Finally, the boundary awareness and feature enhancement module (BA-FEM) is used to extract the retinal layer boundary information and topological order from the low-resolution features of the shallow layer. Our approach has been validated on two public datasets, and Dice scores were 87.61% and 92.44, respectively, both outperforming other state-of-the-art technologies.
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Affiliation(s)
- Yu Chen
- The School of Mechatronics Engineering, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China
| | - XueHe Zhang
- The School of Mechatronics Engineering, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China
| | - Jiahui Yang
- The School of Mechatronics Engineering, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China
| | - Gang Han
- The School of Mechatronics Engineering, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China
| | - He Zhang
- The School of Mechatronics Engineering, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China
| | - MingZhu Lai
- The School of Mathematics and Statistics, Hainan Normal University, Haikou, Hainan 571158, China
| | - Jie Zhao
- The School of Mechatronics Engineering, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China
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Mohammadzadeh V, Wu S, Besharati S, Davis T, Vepa A, Morales E, Edalati K, Rafiee M, Martinyan A, Zhang D, Scalzo F, Caprioli J, Nouri-Mahdavi K. Prediction of Visual Field Progression with Baseline and Longitudinal Structural Measurements Using Deep Learning. Am J Ophthalmol 2024; 262:141-152. [PMID: 38354971 PMCID: PMC11226195 DOI: 10.1016/j.ajo.2024.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 02/03/2024] [Accepted: 02/05/2024] [Indexed: 02/16/2024]
Abstract
PURPOSE Identifying glaucoma patients at high risk of progression based on widely available structural data is an unmet task in clinical practice. We test the hypothesis that baseline or serial structural measures can predict visual field (VF) progression with deep learning (DL). DESIGN Development of a DL algorithm to predict VF progression. METHODS 3,079 eyes (1,765 patients) with various types of glaucoma and ≥5 VFs, and ≥3 years of follow-up from a tertiary academic center were included. Serial VF mean deviation (MD) rates of change were estimated with linear-regression. VF progression was defined as negative MD slope with p<0.05. A Siamese Neural Network with ResNet-152 backbone pre-trained on ImageNet was designed to predict VF progression using serial optic-disc photographs (ODP), and baseline retinal nerve fiber layer (RNFL) thickness. We tested the model on a separate dataset (427 eyes) with RNFL data from different OCT. The Main Outcome Measure was Area under ROC curve (AUC). RESULTS Baseline average (SD) MD was 3.4 (4.9)dB. VF progression was detected in 900 eyes (29%). AUC (95% CI) for model incorporating baseline ODP and RNFL thickness was 0.813 (0.757-0.869). After adding the second and third ODPs, AUC increased to 0.860 and 0.894, respectively (p<0.027). This model also had highest AUC (0.911) for predicting fast progression (MD rate <1.0 dB/year). Model's performance was similar when applied to second dataset using RNFL data from another OCT device (AUC=0.893; 0.837-0.948). CONCLUSIONS DL model predicted VF progression with clinically relevant accuracy using baseline RNFL thickness and serial ODPs and can be implemented as a clinical tool after further validation.
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Affiliation(s)
- Vahid Mohammadzadeh
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - Sean Wu
- Department of Computer Science, Pepperdine University (S.W., F.S.), Malibu, California, USA
| | - Sajad Besharati
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - Tyler Davis
- Department of Computer Science, University of California Los Angeles (T.D., A.V., F.S.), Los Angeles, California, USA
| | - Arvind Vepa
- Department of Computer Science, University of California Los Angeles (T.D., A.V., F.S.), Los Angeles, California, USA
| | - Esteban Morales
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - Kiumars Edalati
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - Mahshad Rafiee
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - Arthur Martinyan
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - David Zhang
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - Fabien Scalzo
- Department of Computer Science, Pepperdine University (S.W., F.S.), Malibu, California, USA; Department of Computer Science, University of California Los Angeles (T.D., A.V., F.S.), Los Angeles, California, USA
| | - Joseph Caprioli
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - Kouros Nouri-Mahdavi
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA.
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9
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Wu JH, Moghimi S, Walker E, Nishida T, Brye N, Mahmoudinezhad G, Liebmann JM, Fazio M, Girkin CA, Zangwill LM, Weinreb RN. Time to Glaucoma Progression Detection by Optical Coherence Tomography in Individuals of African and European Descents. Am J Ophthalmol 2024; 260:60-69. [PMID: 38061585 PMCID: PMC11684426 DOI: 10.1016/j.ajo.2023.12.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 11/30/2023] [Accepted: 12/01/2023] [Indexed: 01/14/2024]
Abstract
PURPOSE To examine the time to detectable retinal nerve fiber layer thickness (RNFLT) progression by optical coherence tomography (OCT) among glaucoma patients of African descent (AD) and European descent (ED). DESIGN Retrospective cohort study. METHODS AD and ED glaucoma eyes from the Diagnostic Innovations in Glaucoma Study (DIGS)/African Descent and Glaucoma Evaluation Study (ADAGES) with ≥2 years/4 visits of optic nerve head RNFLT measurements were included after homogenization on age, diagnosis, and baseline visual field (VF) measurement. RNFLT variability estimates based on linear mixed-effects models were used to simulate longitudinal RNFLT data for both races. Times to trend-based RNFLT progression detection were calculated under standardized scenarios (same RNFLT baseline/thinning rates for both races) and real-world scenarios (AD and ED cohort-specific RNFLT baseline/thinning rates). RESULTS We included 332 and 542 eyes (216 and 317 participants) of AD and ED, respectively. In standardized scenarios, the time to detect RNFLT progression appeared to be similar (difference, <0.2 years) for AD and ED across different assumed RNFLT thinning rates/baseline. In real-world scenarios, compared to ED, AD had a faster RNFLT thinning rate (-0.8 vs -0.6 µm/y) and thicker baseline RNFLT (84.6 vs 81.8 µm). With a faster thinning rate, the mean (SD) time to progression detection was shorter in AD (4.8 [2.0] vs ED: 5.4 [2.4] years), and the 5-year progression rate appeared to be higher (AD: 59% vs ED: 47%). CONCLUSIONS Time to progression detection was similar for both races when assuming identical RNFLT baseline/thinning rates, and shorter in AD eyes under real-world simulation when AD had faster RNFLT thinning. In contrast to prior results on VF, which detected progression later in AD eyes than in ED eyes, OCT may detect progression more consistently across these races.
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Affiliation(s)
- Jo-Hsuan Wu
- From the Hamilton Glaucoma Center (J.-H.W., S.M., E.W., T.N., N.B., G.M., L.M.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Sasan Moghimi
- From the Hamilton Glaucoma Center (J.-H.W., S.M., E.W., T.N., N.B., G.M., L.M.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Evan Walker
- From the Hamilton Glaucoma Center (J.-H.W., S.M., E.W., T.N., N.B., G.M., L.M.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Takashi Nishida
- From the Hamilton Glaucoma Center (J.-H.W., S.M., E.W., T.N., N.B., G.M., L.M.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Nicole Brye
- From the Hamilton Glaucoma Center (J.-H.W., S.M., E.W., T.N., N.B., G.M., L.M.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Golnoush Mahmoudinezhad
- From the Hamilton Glaucoma Center (J.-H.W., S.M., E.W., T.N., N.B., G.M., L.M.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Jeffrey M Liebmann
- Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.), Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York, USA
| | - Massimo Fazio
- Department of Ophthalmology and Vision Sciences (M.F., C.A.G.), Heersink School of Medicine, University of Alabama-Birmingham, Birmingham, Alabama, USA
| | - Christopher A Girkin
- Department of Ophthalmology and Vision Sciences (M.F., C.A.G.), Heersink School of Medicine, University of Alabama-Birmingham, Birmingham, Alabama, USA
| | - Linda M Zangwill
- From the Hamilton Glaucoma Center (J.-H.W., S.M., E.W., T.N., N.B., G.M., L.M.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Robert N Weinreb
- From the Hamilton Glaucoma Center (J.-H.W., S.M., E.W., T.N., N.B., G.M., L.M.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.
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10
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Bradley C, Hou K, Herbert P, Unberath M, Hager G, Boland MV, Ramulu P, Yohannan J. Assessment of linear regression of peripapillary optical coherence tomography retinal nerve fiber layer measurements to forecast glacuoma trajectory. PLoS One 2024; 19:e0296674. [PMID: 38215176 PMCID: PMC10786363 DOI: 10.1371/journal.pone.0296674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 12/15/2023] [Indexed: 01/14/2024] Open
Abstract
Linear regression of optical coherence tomography measurements of peripapillary retinal nerve fiber layer thickness is often used to detect glaucoma progression and forecast future disease course. However, current measurement frequencies suggest that clinicians often apply linear regression to a relatively small number of measurements (e.g., less than a handful). In this study, we estimate the accuracy of linear regression in predicting the next reliable measurement of average retinal nerve fiber layer thickness using Zeiss Cirrus optical coherence tomography measurements of average retinal nerve fiber layer thickness from a sample of 6,471 eyes with glaucoma or glaucoma-suspect status. Linear regression is compared to two null models: no glaucoma worsening, and worsening due to aging. Linear regression on the first M ≥ 2 measurements was significantly worse at predicting a reliable M+1st measurement for 2 ≤ M ≤ 6. This range was reduced to 2 ≤ M ≤ 5 when retinal nerve fiber layer thickness measurements were first "corrected" for scan quality. Simulations based on measurement frequencies in our sample-on average 393 ± 190 days between consecutive measurements-show that linear regression outperforms both null models when M ≥ 5 and the goal is to forecast moderate (75th percentile) worsening, and when M ≥ 3 for rapid (90th percentile) worsening. If linear regression is used to assess disease trajectory with a small number of measurements over short time periods (e.g., 1-2 years), as is often the case in clinical practice, the number of optical coherence tomography examinations needs to be increased.
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Affiliation(s)
- Chris Bradley
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Kaihua Hou
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Patrick Herbert
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Mathias Unberath
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Greg Hager
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Michael V. Boland
- Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts, United States of America
| | - Pradeep Ramulu
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
| | - Jithin Yohannan
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America
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11
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Hood DC, La Bruna S, Leshno A, Gomide GA, Kim MJ, Cioffi GA, Liebmann JM, De Moraes CG, Tsamis E. A Model of Progression to Help Identify Macular Damage Due to Glaucoma. Invest Ophthalmol Vis Sci 2023; 64:8. [PMID: 38060217 PMCID: PMC10709805 DOI: 10.1167/iovs.64.15.8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 11/10/2023] [Indexed: 12/08/2023] Open
Abstract
The central macula contains a thick donut shaped region of the ganglion cell layer (GCL) that surrounds the fovea. This region, which is about 12 degrees (3.5 mm) in diameter, is essential for everyday functions such as driving, reading, and face recognition. Here, we describe a model of progression of glaucomatous damage to this GCL donut. This model is based upon assumptions supported by the literature, and it predicts the patterns of glaucomatous damage to the GCL donut, as seen with optical coherence tomography (OCT). After describing the assumptions and predictions of this model, we test the model against data from our laboratory, as well as from the literature. Finally, three uses of the model are illustrated. One, it provides an aid to help clinicians focus on the essential central macula and to alert them to look for other, non-glaucomatous causes, when the GCL damage does not fit the pattern predicted by the model. Second, the patterns of progression predicted by the model suggest alternative end points for clinical trials. Finally, the model provides a heuristic for future research concerning the anatomic basis of glaucomatous damage.
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Affiliation(s)
- Donald C. Hood
- Department of Psychology, Columbia University, New York, New York, United States
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Columbia University Irving Medical Center, New York, New York, United States
| | - Sol La Bruna
- University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States
| | - Ari Leshno
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Columbia University Irving Medical Center, New York, New York, United States
- Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Gabriel A. Gomide
- Vagelos College of Physicians and Surgeons, New York, New York, United States
| | - Mi Jeung Kim
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Columbia University Irving Medical Center, New York, New York, United States
- Department of Ophthalmology, Hangil Eye Hospital, Incheon, Republic of Korea
- Department of Ophthalmology, Catholic Kwandong University College of Medicine, Incheon, Republic of Korea
| | - George A. Cioffi
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Columbia University Irving Medical Center, New York, New York, United States
| | - Jeffrey M. Liebmann
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Columbia University Irving Medical Center, New York, New York, United States
| | - Carlos Gustavo De Moraes
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Columbia University Irving Medical Center, New York, New York, United States
| | - Emmanouil Tsamis
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Columbia University Irving Medical Center, New York, New York, United States
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12
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Hou K, Bradley C, Herbert P, Johnson C, Wall M, Ramulu PY, Unberath M, Yohannan J. Predicting Visual Field Worsening with Longitudinal OCT Data Using a Gated Transformer Network. Ophthalmology 2023; 130:854-862. [PMID: 37003520 PMCID: PMC10524436 DOI: 10.1016/j.ophtha.2023.03.019] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 03/16/2023] [Accepted: 03/24/2023] [Indexed: 04/03/2023] Open
Abstract
PURPOSE To identify visual field (VF) worsening from longitudinal OCT data using a gated transformer network (GTN) and to examine how GTN performance varies for different definitions of VF worsening and different stages of glaucoma severity at baseline. DESIGN Retrospective longitudinal cohort study. PARTICIPANTS A total of 4211 eyes (2666 patients) followed up at the Johns Hopkins Wilmer Eye Institute with at least 5 reliable VF results and 1 reliable OCT scan within 1 year of each reliable VF test. METHODS For each eye, we used 3 trend-based methods (mean deviation [MD] slope, VF index slope, and pointwise linear regression) and 3 event-based methods (Guided Progression Analysis, Collaborative Initial Glaucoma Treatment Study scoring system, and Advanced Glaucoma Intervention Study [AGIS] scoring system) to define VF worsening. Additionally, we developed a "majority of 6" algorithm (M6) that classifies an eye as worsening if 4 or more of the 6 aforementioned methods classified the eye as worsening. Using these 7 reference standards for VF worsening, we trained 7 GTNs that accept a series of at least 5 as input OCT scans and provide as output a probability of VF worsening. Gated transformer network performance was compared with non-deep learning models with the same serial OCT input from previous studies-linear mixed-effects models (MEMs) and naive Bayes classifiers (NBCs)-using the same training sets and reference standards as for the GTN. MAIN OUTCOME MEASURES Area under the receiver operating characteristic curve (AUC). RESULTS The M6 labeled 63 eyes (1.50%) as worsening. The GTN achieved an AUC of 0.97 (95% confidence interval, 0.88-1.00) when trained with M6. Gated transformer networks trained and optimized with the other 6 reference standards showed an AUC ranging from 0.78 (MD slope) to 0.89 (AGIS). The 7 GTNs outperformed all 7 MEMs and all 7 NBCs accordingly. Gated transformer network performance was worse for eyes with more severe glaucoma at baseline. CONCLUSIONS Gated transformer network models trained with OCT data may be used to identify VF worsening. After further validation, implementing such models in clinical practice may allow us to track functional worsening of glaucoma with less onerous structural testing. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
- Kaihua Hou
- Johns Hopkins University, Baltimore, Maryland
| | | | | | | | | | | | - Mathias Unberath
- Johns Hopkins University, Baltimore, Maryland; Johns Hopkins Medicine, Baltimore, Maryland
| | - Jithin Yohannan
- Johns Hopkins University, Baltimore, Maryland; Johns Hopkins Medicine, Baltimore, Maryland.
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13
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Shiga Y, Nishida T, Jeoung JW, Di Polo A, Fortune B. Optical Coherence Tomography and Optical Coherence Tomography Angiography: Essential Tools for Detecting Glaucoma and Disease Progression. FRONTIERS IN OPHTHALMOLOGY 2023; 3:1217125. [PMID: 37982032 PMCID: PMC10655832 DOI: 10.3389/fopht.2023.1217125] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 07/03/2023] [Indexed: 11/21/2023]
Abstract
Early diagnosis and detection of disease progression are critical to successful therapeutic intervention in glaucoma, the leading cause of irreversible blindness worldwide. Optical coherence tomography (OCT) is a non-invasive imaging technique that allows objective quantification in vivo of key glaucomatous structural changes in the retina and the optic nerve head (ONH). Advances in OCT technology have increased the scan speed and enhanced image quality, contributing to early glaucoma diagnosis and monitoring, as well as the visualization of critically important structures deep within the ONH, such as the lamina cribrosa. OCT angiography (OCTA) is a dye-free technique for noninvasively assessing ocular microvasculature, including capillaries within each plexus serving the macula, peripapillary retina and ONH regions, as well as the deeper vessels of the choroid. This layer-specific assessment of the microvasculature has provided evidence that retinal and choroidal vascular impairments can occur during early stages of glaucoma, suggesting that OCTA-derived measurements could be used as biomarkers for enhancing detection of glaucoma and its progression, as well as to reveal novel insights about pathophysiology. Moreover, these innovations have demonstrated that damage to the macula, a critical region for the vision-related quality of life, can be observed in the early stages of glaucomatous eyes, leading to a paradigm shift in glaucoma monitoring. Other advances in software and hardware, such as artificial intelligence-based algorithms, adaptive optics, and visible-light OCT, may further benefit clinical management of glaucoma in the future. This article reviews the utility of OCT and OCTA for glaucoma diagnosis and disease progression detection, emphasizes the importance of detecting macula damage in glaucoma, and highlights the future perspective of OCT and OCTA. We conclude that the OCT and OCTA are essential glaucoma detection and monitoring tools, leading to clinical and economic benefits for patients and society.
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Affiliation(s)
- Yukihiro Shiga
- Neuroscience Division, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Québec H2X 0A9, Canada
- Department of Neuroscience, Université de Montréal, Montréal, Québec H3C 3J7, Canada
| | - Takashi Nishida
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California 92093, USA
| | - Jin Wook Jeoung
- Department of Ophthalmology, Seoul National University Hospital, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Adriana Di Polo
- Neuroscience Division, Centre de Recherche du Centre Hospitalier de l'Université de Montréal, Montréal, Québec H2X 0A9, Canada
- Department of Neuroscience, Université de Montréal, Montréal, Québec H3C 3J7, Canada
| | - Brad Fortune
- Discoveries in Sight Research Laboratories, Devers Eye Institute and Legacy Research Institute, Legacy Health, 1225 NE Second Avenue, Portland, Oregon 97232, USA
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14
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Bradley C, Herbert P, Hou K, Unberath M, Ramulu P, Yohannan J. Comparing the Accuracy of Peripapillary OCT Scans and Visual Fields to Detect Glaucoma Worsening. Ophthalmology 2023; 130:631-639. [PMID: 36754173 PMCID: PMC10200740 DOI: 10.1016/j.ophtha.2023.01.021] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Revised: 01/17/2023] [Accepted: 01/31/2023] [Indexed: 02/08/2023] Open
Abstract
PURPOSE To compare the accuracy of detecting moderate and rapid rates of glaucoma worsening over a 2-year period with different numbers of OCT scans and visual field (VF) tests in a large sample of glaucoma and glaucoma suspect eyes. DESIGN Descriptive and simulation study. PARTICIPANTS The OCT sample comprised 12 150 eyes from 7392 adults with glaucoma or glaucoma suspect status followed up at the Wilmer Eye Institute from 2013 through 2021. The VF sample comprised 20 583 eyes from 10 958 adults from the same database. All eyes had undergone at least 5 measurements over follow-up from the Zeiss Cirrus OCT or Humphrey Field Analyzer. METHODS Within-eye rates of change in retinal nerve fiber layer (RNFL) thickness and mean deviation (MD) were measured using linear regression. For each measured rate, simulated measurements of RNFL thickness and MD were generated using the distributions of residuals. Simulated rates of change for different numbers of OCT scans and VF tests over a 2-year period were used to estimate the accuracy of detecting moderate (75th percentile) and rapid (90th percentile) worsening for OCT and VF. Accuracy was defined as the percentage of simulated eyes in which the true rate of worsening (the rate without measurement error) was at or less than a criterion rate (e.g., 75th or 90th percentile). MAIN OUTCOME MEASURES The accuracy of diagnosing moderate and rapid rates of glaucoma worsening for different numbers of OCT scans and VF tests over a 2-year period. RESULTS Accuracy was less than 50% for both OCT and VF when diagnosing worsening after a 2-year period. OCT accuracy was 5 to 10 percentage points higher than VF accuracy at detecting moderate worsening and 10 to 15 percentage points higher for rapid worsening. Accuracy increased by more than 17 percentage points when using both OCT and VF to detect worsening, that is, when relying on either OCT or VF to be accurate. CONCLUSIONS More frequent OCT scans and VF tests are needed to improve the accuracy of diagnosing glaucoma worsening. Accuracy greatly increases when relying on both OCT and VF to detect worsening. FINANCIAL DISCLOSURE(S) The author(s) have no proprietary or commercial interest in any materials discussed in this article.
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Affiliation(s)
- Chris Bradley
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| | - Patrick Herbert
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Kaihua Hou
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Mathias Unberath
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Pradeep Ramulu
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jithin Yohannan
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland; Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland
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15
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Mariottoni EB, Datta S, Shigueoka LS, Jammal AA, Tavares IM, Henao R, Carin L, Medeiros FA. Deep Learning-Assisted Detection of Glaucoma Progression in Spectral-Domain OCT. Ophthalmol Glaucoma 2023; 6:228-238. [PMID: 36410708 PMCID: PMC10278200 DOI: 10.1016/j.ogla.2022.11.004] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Revised: 10/24/2022] [Accepted: 11/09/2022] [Indexed: 05/26/2023]
Abstract
PURPOSE To develop and validate a deep learning (DL) model for detection of glaucoma progression using spectral-domain (SD)-OCT measurements of retinal nerve fiber layer (RNFL) thickness. DESIGN Retrospective cohort study. PARTICIPANTS A total of 14 034 SD-OCT scans from 816 eyes from 462 individuals. METHODS A DL convolutional neural network was trained to assess SD-OCT RNFL thickness measurements of 2 visits (a baseline and a follow-up visit) along with time between visits to predict the probability of glaucoma progression. The ground truth was defined by consensus from subjective grading by glaucoma specialists. Diagnostic performance was summarized by the area under the receiver operator characteristic curve (AUC), sensitivity, and specificity, and was compared with conventional trend-based analyses of change. Interval likelihood ratios were calculated to determine the impact of DL model results in changing the post-test probability of progression. MAIN OUTCOME MEASURES The AUC, sensitivity, and specificity of the DL model. RESULTS The DL model had an AUC of 0.938 (95% confidence interval [CI], 0.921-0.955), with sensitivity of 87.3% (95% CI, 83.6%-91.6%) and specificity of 86.4% (95% CI, 79.9%-89.6%). When matched for the same specificity, the DL model significantly outperformed trend-based analyses. Likelihood ratios for the DL model were associated with large changes in the probability of progression in the vast majority of SD-OCT tests. CONCLUSIONS A DL model was able to assess the probability of glaucomatous structural progression from SD-OCT RNFL thickness measurements. The model agreed well with expert judgments and outperformed conventional trend-based analyses of change, while also providing indication of the likely locations of change. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
- Eduardo B Mariottoni
- Vision, Imaging, and Performance (VIP) Laboratory, Duke Eye Center, Duke University, Durham, North Carolina; Department of Ophthalmology, Federal University of São Paulo, São Paulo, Brazil
| | - Shounak Datta
- Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina
| | - Leonardo S Shigueoka
- Vision, Imaging, and Performance (VIP) Laboratory, Duke Eye Center, Duke University, Durham, North Carolina
| | - Alessandro A Jammal
- Vision, Imaging, and Performance (VIP) Laboratory, Duke Eye Center, Duke University, Durham, North Carolina
| | - Ivan M Tavares
- Department of Ophthalmology, Federal University of São Paulo, São Paulo, Brazil
| | - Ricardo Henao
- Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina
| | - Lawrence Carin
- Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina
| | - Felipe A Medeiros
- Vision, Imaging, and Performance (VIP) Laboratory, Duke Eye Center, Duke University, Durham, North Carolina; Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina.
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16
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Bradley C, Hou K, Herbert P, Unberath M, Boland MV, Ramulu P, Yohannan J. Evidence-Based Guidelines for the Number of Peripapillary OCT Scans Needed to Detect Glaucoma Worsening. Ophthalmology 2023; 130:39-47. [PMID: 35932839 PMCID: PMC9780153 DOI: 10.1016/j.ophtha.2022.07.025] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 07/11/2022] [Accepted: 07/25/2022] [Indexed: 01/06/2023] Open
Abstract
PURPOSE To estimate the number of OCT scans necessary to detect moderate and rapid rates of retinal nerve fiber layer (RNFL) thickness worsening at different levels of accuracy using a large sample of glaucoma and glaucoma-suspect eyes. DESIGN Descriptive and simulation study. PARTICIPANTS Twelve thousand one hundred fifty eyes from 7392 adult patients with glaucoma or glaucoma-suspect status followed up at the Wilmer Eye Institute from 2013 through 2021. All eyes had at least 5 measurements of RNFL thickness on the Cirrus OCT (Carl Zeiss Meditec) with signal strength of 6 or more. METHODS Rates of RNFL worsening for average RNFL thickness and for the 4 quadrants were measured using linear regression. Simulations were used to estimate the accuracy of detecting worsening-defined as the percentage of patients in whom the true rate of RNFL worsening was at or less than different criterion rates of worsening when the OCT-measured rate was also at or less than these criterion rates-for two different measurement strategies: evenly spaced (equal time intervals between measurements) and clustered (approximately half the measurements at each end point of the period). MAIN OUTCOME MEASURES The 75th percentile (moderate) and 90th percentile (rapid) rates of RNFL worsening for average RNFL thickness and the accuracy of diagnosing worsening at these moderate and rapid rates. RESULTS The 75th and 90th percentile rates of worsening for average RNFL thickness were -1.09 μm/year and -2.35 μm/year, respectively. Simulations showed that, for the average measurement frequency in our sample of approximately 3 OCT scans over a 2-year period, moderate and rapid RNFL worsening were diagnosed accurately only 47% and 40% of the time, respectively. Estimates for the number of OCT scans needed to achieve a range of accuracy levels are provided. For example, 60% accuracy requires 7 measurements to detect both moderate and rapid worsening within a 2-year period if the more efficient clustered measurement strategy is used. CONCLUSIONS To diagnose RNFL worsening more accurately, the number of OCT scans must be increased compared with current clinical practice. A clustered measurement strategy reduces the number of scans required compared with evenly spacing measurements.
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Affiliation(s)
- Chris Bradley
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland.
| | - Kaihua Hou
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Patrick Herbert
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Mathias Unberath
- Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Michael V Boland
- Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Pradeep Ramulu
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Jithin Yohannan
- Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland; Malone Center of Engineering in Healthcare, Johns Hopkins University School of Medicine, Baltimore, Maryland
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17
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Mahmoudinezhad G, Moghimi S, Proudfoot JA, Brye N, Nishida T, Yarmohammadi A, Kamalipour A, Zangwill LM, Weinreb RN. Effect of Testing Frequency on the Time to Detect Glaucoma Progression With Optical Coherence Tomography (OCT) and OCT Angiography. Am J Ophthalmol 2023; 245:184-192. [PMID: 36096181 PMCID: PMC11855188 DOI: 10.1016/j.ajo.2022.08.030] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 11/19/2022]
Abstract
PURPOSE To determine how the frequency of testing affects the time required to detect statistically significant glaucoma progression for circumpapillary retinal nerve fiber layer (cpRNFL) with optical coherence tomography (OCT) and circumpapillary capillary density (cpCD) with OCT angiography (OCTA). DESIGN Retrospective, observational cohort study. METHODS In this longitudinal study, 156 eyes of 98 patients with glaucoma followed up over an average of 3.5 years were enrolled. Participants with 4 or more OCT and OCTA tests were included to measure the longitudinal rates of cpRNFL thickness and cpCD change over time using linear regression. Estimates of variability were then used to re-create real-world cpRNFL and cpCD data by computer simulation to evaluate the time required to detect progression for various loss rates and different testing frequencies. RESULTS The time required to detect a statistically significant negative cpRNFL and cpCD slope decreased as the testing frequency increased, albeit not proportionally. cpCD detected progression slightly earlier than cpRNFL. Eighty percent of eyes with a cpCD loss of -1%/y were detected after 6.0, 4.2, and 4 years when testing was performed 1, 2, and 3 times per year, respectively. Progression in 80% of eyes with a cpRNFL loss of -1 µm/y was detected after 6.3, 5.0, and 4.2 years, respectively. CONCLUSIONS cpRNFL and cpCD are comparable in detecting progression. As there were only small changes in the time to detect progression when testing increased from 2 to 3 times per year, testing twice per year may provide sufficient information for detecting progression with either OCT or OCTA in clinical settings.
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Affiliation(s)
- Golnoush Mahmoudinezhad
- From the Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Sasan Moghimi
- From the Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - James A Proudfoot
- From the Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Nicole Brye
- From the Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Takashi Nishida
- From the Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Adeleh Yarmohammadi
- From the Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Alireza Kamalipour
- From the Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Linda M Zangwill
- From the Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Robert N Weinreb
- From the Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA..
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18
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Kurysheva NI, Nikitina AD. [Optical coherence tomography and optical coherence tomography angiography for detecting glaucoma progression. Part 1. Study methods, measurement variability and the role of age-related changes]. Vestn Oftalmol 2023; 139:122-128. [PMID: 36924524 DOI: 10.17116/oftalma2023139011122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
Abstract
This paper reviews the literature on the role of optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) in the diagnosis of glaucoma and considers the significance of evaluating retinal nerve fiber layer and ganglion cell complex in assessment of glaucoma progression, variability and reproducibility of the method, as well as the influence of age-related retinal changes on the results, analyzes the role of OCTA in glaucoma monitoring. Optical coherence tomography is a modern standard for glaucoma diagnosis and monitoring, and OCTA shows high potential as an auxiliary diagnostic tool.
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Affiliation(s)
- N I Kurysheva
- Medical Biological University of Innovations and Continuing Education of the Federal Biophysical Center named after A.I. Burnazyan, Moscow, Russia.,Ophthalmological Center of the Federal Medical-Biological Agency - Federal Medical Biophysical Center named after A.I. Burnazyan, Moscow, Russia
| | - A D Nikitina
- Medical Biological University of Innovations and Continuing Education of the Federal Biophysical Center named after A.I. Burnazyan, Moscow, Russia.,Ophthalmological Center of the Federal Medical-Biological Agency - Federal Medical Biophysical Center named after A.I. Burnazyan, Moscow, Russia
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19
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Kurysheva NI, Nikitina AD. [Optical coherence tomography and optical coherence tomography angiography for detecting glaucoma progression. Part 2. Clinical and functional correlations, monitoring of advanced glaucoma and limitations of the method]. Vestn Oftalmol 2023; 139:76-83. [PMID: 37067935 DOI: 10.17116/oftalma202313902176] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
The purpose of this study is to analyze the literature on the role of optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) in the diagnosis of glaucoma. This review considers the structural and functional correlations observed during the progression of glaucomatous optic neuropathy, as well as the capabilities of the method in late glaucoma, describes the strengths and weaknesses of OCT and OCTA, and pays particular attention to the role of OCT in assessing the effectiveness of treatment. Optical coherence tomography is the main method for determining the progression of glaucoma, which plays a key role in the choice of treatment algorithm. However, the use of OCT in far advanced glaucoma has certain particularities and limitations. OCTA can be helpful in overcoming this problem.
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Affiliation(s)
- N I Kurysheva
- Medical Biological University of Innovations and Continuing Education of the Federal Medical Biophysical Center named after A.I. Burnazyan, Moscow, Russia
- Ophthalmological Center of the Federal Medical-Biological Agency of the Federal Medical Biophysical Center named after A.I. Burnazyan, Moscow, Russia
| | - A D Nikitina
- Medical Biological University of Innovations and Continuing Education of the Federal Medical Biophysical Center named after A.I. Burnazyan, Moscow, Russia
- Ophthalmological Center of the Federal Medical-Biological Agency of the Federal Medical Biophysical Center named after A.I. Burnazyan, Moscow, Russia
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20
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Mohammadzadeh V, Su E, Shi L, Coleman AL, Law SK, Caprioli J, Weiss RE, Nouri-Mahdavi K. Multivariate Longitudinal Modeling of Macular Ganglion Cell Complex: Spatiotemporal Correlations and Patterns of Longitudinal Change. OPHTHALMOLOGY SCIENCE 2022; 2:100187. [PMID: 36245763 PMCID: PMC9559093 DOI: 10.1016/j.xops.2022.100187] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 05/20/2022] [Accepted: 06/09/2022] [Indexed: 01/11/2023]
Abstract
Purpose To investigate spatiotemporal correlations among ganglion cell complex (GCC) superpixel thickness measurements and explore underlying patterns of longitudinal change across the macular region. Design Longitudinal cohort study. Subjects One hundred eleven eyes from 111 subjects from the Advanced Glaucoma Progression Study with ≥ 4 visits and ≥ 2 years of follow-up. Methods We further developed our proposed Bayesian hierarchical model for studying longitudinal GCC thickness changes across macular superpixels in a cohort of glaucoma patients. Global priors were introduced for macular superpixel parameters to combine data across superpixels and better estimate population slopes and intercepts. Main Outcome Measures Bayesian residual analysis to inspect cross-superpixel correlations for subject random effects and residuals. Principal component analysis (PCA) to explore underlying patterns of longitudinal macular change. Results Average (standard deviation [SD]) follow-up and baseline 10-2 visual field mean deviation were 3.6 (0.4) years and -8.9 (5.9) dB, respectively. Superpixel-level random effects and residuals had the greatest correlations with nearest neighbors; correlations were higher in the superior than in the inferior region and strongest among random intercepts, followed by random slopes, residuals, and residual SDs. PCA of random intercepts showed a first large principal component (PC) across superpixels that approximated a global intercept, a second PC that contrasted the superior and inferior macula, and a third PC, contrasting inner and nasal superpixels with temporal and peripheral superpixels. PCs for slopes, residual SDs, and residuals were remarkably similar to those of random intercepts. Conclusions Introduction of cross-superpixel random intercepts and slopes is expected to improve estimation of population and subject parameters. Further model enhancement may be possible by including cross-superpixel random effects and correlations to address spatiotemporal relationships in longitudinal data sets.
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Affiliation(s)
- Vahid Mohammadzadeh
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Erica Su
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California
| | - Lynn Shi
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Anne L. Coleman
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Simon K. Law
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Joseph Caprioli
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Robert E. Weiss
- Department of Biostatistics, Fielding School of Public Health, University of California Los Angeles, Los Angeles, California
| | - Kouros Nouri-Mahdavi
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California,Correspondence: Kouros Nouri-Mahdavi, MD, MS, 100 Stein Plaza, Los Angeles, CA, 90095.
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21
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Bartlett RL, Frost BE, Mortlock KE, Fergusson JR, White N, Morgan JE, North RV, Albon J. Quantifying biomarkers of axonal degeneration in early glaucoma to find the disc at risk. Sci Rep 2022; 12:9366. [PMID: 35672326 PMCID: PMC9174204 DOI: 10.1038/s41598-022-12036-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Accepted: 03/31/2022] [Indexed: 12/03/2022] Open
Abstract
To evaluate regional axonal-related parameters as a function of disease stage in primary open angle glaucoma (POAG) and visual field (VF) sensitivity. Spectral domain optical coherence tomography was used to acquire 20° scans of POAG (n = 117) or healthy control (n = 52) human optic nerve heads (ONHs). Region specific and mean nerve fibre layer (NFL) thicknesses, border NFL and peripapillary NFL, minimum rim width (MRW)/ area (MRA) and prelamina thickness; and volume were compared across POAG disease stages and with visual field sensitivity. Differences identified between early glaucoma (EG), preperimetric glaucoma (PG) and control (C) ONHs included thinner PG prelamina regions than in controls (p < 0.05). Mean border NFL was thinner in EG (p < 0.001) and PG (p = 0.049) compared to control eyes; and EG mean, and inferior and ST, border NFL was thinner than in PG (p < 0.01). Mean, superior and inferior PG peripapillary NFL were thinner than in controls (p < 0.05), and EG ST peripapillary NFL was thinner than in PG (p = 0.023). MRW differences included: PG SN and inferior less than in controls (p < 0.05); thinner EG mean regional, inferior, nasal, and ST MRW versus PG MRW (p < 0.05). Regional border NFL, peripapillary NFL, MRW, MRA, prelamina thickness (except centre, p = 0.127) and prelamina volume (p < 0.05) were significantly associated with VF mean deviation (MD). Novel axon-derived indices hold potential as biomarkers to detect early glaucoma and identify ONHs at risk.
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Affiliation(s)
- R L Bartlett
- School of Optometry and Vision Sciences, Cardiff University, Cardiff, UK
- Cardiff Institute for Tissue Engineering and Repair, Cardiff University, Cardiff, UK
- Vivat Scientia Bioimaging Laboratories, Cardiff University, Cardiff, UK
| | - B E Frost
- School of Optometry and Vision Sciences, Cardiff University, Cardiff, UK
- School of Biosciences, Cardiff University, Cardiff, UK
| | - K E Mortlock
- School of Optometry and Vision Sciences, Cardiff University, Cardiff, UK
- Cardiff Institute for Tissue Engineering and Repair, Cardiff University, Cardiff, UK
- Vivat Scientia Bioimaging Laboratories, Cardiff University, Cardiff, UK
| | - J R Fergusson
- School of Optometry and Vision Sciences, Cardiff University, Cardiff, UK
- Cardiff Institute for Tissue Engineering and Repair, Cardiff University, Cardiff, UK
- Vivat Scientia Bioimaging Laboratories, Cardiff University, Cardiff, UK
| | - N White
- School of Optometry and Vision Sciences, Cardiff University, Cardiff, UK
- Cardiff Institute for Tissue Engineering and Repair, Cardiff University, Cardiff, UK
- Vivat Scientia Bioimaging Laboratories, Cardiff University, Cardiff, UK
| | - J E Morgan
- School of Optometry and Vision Sciences, Cardiff University, Cardiff, UK
| | - R V North
- School of Optometry and Vision Sciences, Cardiff University, Cardiff, UK
- Vivat Scientia Bioimaging Laboratories, Cardiff University, Cardiff, UK
| | - J Albon
- School of Optometry and Vision Sciences, Cardiff University, Cardiff, UK.
- Cardiff Institute for Tissue Engineering and Repair, Cardiff University, Cardiff, UK.
- Vivat Scientia Bioimaging Laboratories, Cardiff University, Cardiff, UK.
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22
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García G, Del Amor R, Colomer A, Verdú-Monedero R, Morales-Sánchez J, Naranjo V. Circumpapillary OCT-focused hybrid learning for glaucoma grading using tailored prototypical neural networks. Artif Intell Med 2021; 118:102132. [PMID: 34412848 DOI: 10.1016/j.artmed.2021.102132] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/21/2021] [Accepted: 06/23/2021] [Indexed: 12/22/2022]
Abstract
Glaucoma is one of the leading causes of blindness worldwide and Optical Coherence Tomography (OCT) is the quintessential imaging technique for its detection. Unlike most of the state-of-the-art studies focused on glaucoma detection, in this paper, we propose, for the first time, a novel framework for glaucoma grading using raw circumpapillary B-scans. In particular, we set out a new OCT-based hybrid network which combines hand-driven and deep learning algorithms. An OCT-specific descriptor is proposed to extract hand-crafted features related to the retinal nerve fibre layer (RNFL). In parallel, an innovative CNN is developed using skip-connections to include tailored residual and attention modules to refine the automatic features of the latent space. The proposed architecture is used as a backbone to conduct a novel few-shot learning based on static and dynamic prototypical networks. The k-shot paradigm is redefined giving rise to a supervised end-to-end system which provides substantial improvements discriminating between healthy, early and advanced glaucoma samples. The training and evaluation processes of the dynamic prototypical network are addressed from two fused databases acquired via Heidelberg Spectralis system. Validation and testing results reach a categorical accuracy of 0.9459 and 0.8788 for glaucoma grading, respectively. Besides, the high performance reported by the proposed model for glaucoma detection deserves a special mention. The findings from the class activation maps are directly in line with the clinicians' opinion since the heatmaps pointed out the RNFL as the most relevant structure for glaucoma diagnosis.
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Affiliation(s)
- Gabriel García
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain.
| | - Rocío Del Amor
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
| | - Adrián Colomer
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
| | - Rafael Verdú-Monedero
- Departamento de Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Juan Morales-Sánchez
- Departamento de Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Valery Naranjo
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
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23
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Thompson AC, Li A, Asrani S. Agreement Between Trend-Based and Qualitative Analysis of the Retinal Nerve Fiber Layer Thickness for Glaucoma Progression on Spectral-Domain Optical Coherence Tomography. Ophthalmol Ther 2021; 10:629-642. [PMID: 34212312 PMCID: PMC8319289 DOI: 10.1007/s40123-021-00355-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2021] [Accepted: 05/27/2021] [Indexed: 11/26/2022] Open
Abstract
INTRODUCTION To evaluate the agreement between trend-based analysis and qualitative assessment of the retinal nerve fiber layer (RNFL) thickness for glaucomatous progression on spectral-domain optical coherence tomography (SDOCT). METHODS Retrospective review of 190 eyes from 103 patients with glaucoma or suspected glaucoma that underwent SDOCT imaging during four consecutive clinic visits. Trend-based progression was characterized by a significantly negative slope. Progression by qualitative analysis was determined by review of raw SDOCT B-scans. RESULTS The slope was significantly greater in those with progression than without progression for both trend-based and qualitative analysis (p < 0.001). However, the qualitative grading classified a significantly greater proportion of eyes as progressing compared to trend-based analysis in both the superotemporal (ST) (23.2% vs. 10.5%, p = 0.001) and inferotemporal (IT) RNFL (27.4% vs 8.4%, p < 0.001). The trend-based and qualitative classifications of progression showed poor agreement in both the ST (kappa = 0.0135) and IT RNFL (kappa = 0.1222). The agreement between trend-based and qualitative analysis was lower for eyes with artifacts (ST = 58.11%; IT = 68.7%) than those without artifacts (ST = 80.2%; IT = 74.8%). Moreover, among eyes with artifacts, there was no significant difference in slope between those qualitatively categorized as progressing versus not progressing (p > 0.05). CONCLUSIONS Poor agreement was found between a trend-based and qualitative analysis of change in RNFL on SDOCT. Careful qualitative review of SDOCT imaging may identify specific areas of glaucoma progression not captured by trend-based methods, especially in the presence of artifacts. Such an approach may also prove useful for detecting glaucoma progression in a clinical setting when there are few data points available.
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Affiliation(s)
- Atalie C Thompson
- Department of Ophthalmology, Duke University, Box 3802, Durham, NC, 27710, USA
| | - Ang Li
- Cleveland Clinic, Cleveland, OH, USA
| | - Sanjay Asrani
- Department of Ophthalmology, Duke University, Box 3802, Durham, NC, 27710, USA.
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24
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Baxter SL, Saseendrakumar BR, Paul P, Kim J, Bonomi L, Kuo TT, Loperena R, Ratsimbazafy F, Boerwinkle E, Cicek M, Clark CR, Cohn E, Gebo K, Mayo K, Mockrin S, Schully SD, Ramirez A, Ohno-Machado L. Predictive Analytics for Glaucoma Using Data From the All of Us Research Program. Am J Ophthalmol 2021; 227:74-86. [PMID: 33497675 PMCID: PMC8184631 DOI: 10.1016/j.ajo.2021.01.008] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Revised: 01/02/2021] [Accepted: 01/06/2021] [Indexed: 12/21/2022]
Abstract
PURPOSE To (1) use All of Us (AoU) data to validate a previously published single-center model predicting the need for surgery among individuals with glaucoma, (2) train new models using AoU data, and (3) share insights regarding this novel data source for ophthalmic research. DESIGN Development and evaluation of machine learning models. METHODS Electronic health record data were extracted from AoU for 1,231 adults diagnosed with primary open-angle glaucoma. The single-center model was applied to AoU data for external validation. AoU data were then used to train new models for predicting the need for glaucoma surgery using multivariable logistic regression, artificial neural networks, and random forests. Five-fold cross-validation was performed. Model performance was evaluated based on area under the receiver operating characteristic curve (AUC), accuracy, precision, and recall. RESULTS The mean (standard deviation) age of the AoU cohort was 69.1 (10.5) years, with 57.3% women and 33.5% black, significantly exceeding representation in the single-center cohort (P = .04 and P < .001, respectively). Of 1,231 participants, 286 (23.2%) needed glaucoma surgery. When applying the single-center model to AoU data, accuracy was 0.69 and AUC was only 0.49. Using AoU data to train new models resulted in superior performance: AUCs ranged from 0.80 (logistic regression) to 0.99 (random forests). CONCLUSIONS Models trained with national AoU data achieved superior performance compared with using single-center data. Although AoU does not currently include ophthalmic imaging, it offers several strengths over similar big-data sources such as claims data. AoU is a promising new data source for ophthalmic research.
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Affiliation(s)
- Sally L Baxter
- From the Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, (S.L.B., B.R.S.), La Jolla, California; UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California.
| | - Bharanidharan Radha Saseendrakumar
- From the Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, (S.L.B., B.R.S.), La Jolla, California; UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California
| | - Paulina Paul
- UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California
| | - Jihoon Kim
- UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California
| | - Luca Bonomi
- UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California
| | - Tsung-Ting Kuo
- UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California
| | - Roxana Loperena
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee (R.L., F.R.)
| | - Francis Ratsimbazafy
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee (R.L., F.R.)
| | - Eric Boerwinkle
- School of Public Health, The University of Texas Health Science Center at Houston, Houston, Texas (E.B.)
| | - Mine Cicek
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota (M.C.)
| | - Cheryl R Clark
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts (C.R.C.)
| | - Elizabeth Cohn
- Hunter-Bellevue School of Nursing, Hunter College City University of New York, New York, New York (E.C.)
| | - Kelly Gebo
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, Maryland
| | - Kelsey Mayo
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee (R.L., F.R.)
| | - Stephen Mockrin
- Life Sciences Division, Leidos, Inc, Frederick, (S.M.), Maryland
| | - Sheri D Schully
- All of Us Research Program, National Institutes of Health, Bethesda (K.M., S.S.), Bethesda, Maryland
| | - Andrea Ramirez
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee (A.R.)
| | - Lucila Ohno-Machado
- UCSD Health Department of Biomedical Informatics, University of California San Diego, (S.L.B., B.R.S., P.P., J.K., L.B., T.-T.K., L.O.-M.), La Jolla, California; Division of Health Services Research and Development, Veterans Affairs San Diego Healthcare System, La Jolla, California (L.O.-M.), USA
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Koenig SF, Hirneiss CW. Changes of Neuroretinal Rim and Retinal Nerve Fiber Layer Thickness Assessed by Optical Coherence Tomography After Filtration Surgery in Glaucomatous Eyes. Clin Ophthalmol 2021; 15:2335-2344. [PMID: 34113077 PMCID: PMC8184240 DOI: 10.2147/opth.s298045] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 03/15/2021] [Indexed: 12/21/2022] Open
Abstract
Purpose Lowering the intraocular pressure (IOP) in patients with primary open-angle glaucoma (POAG) with filtration surgery can induce morphological changes to the bulbus and structures of the retina. In this study, we have evaluated changes of Bruch's membrane-based parameters and retinal nerve fiber layer (RNFL) derived by spectral-domain optical coherence tomography (SD-OCT) in eyes that have undergone glaucoma filtration surgery. Patients and Methods SD-OCT imaging of the optic nerve head (ONH) and of the RNFL was performed in 54 eyes of 54 patients with medically uncontrolled POAG before and after IOP-lowering surgery (trabeculectomy or deep sclerectomy). The ONH parameter minimum rim width (MRW) and the size of the Bruch's membrane opening (BMO-Area) were derived from 24 radial B-scans centered on the ONH. Results The average preoperative IOP was 23.1 ± 7.5 mmHg. One month postoperatively, the average IOP decreased to 12.1 ± 4.6 mmHg (p < 0.01), which caused a significant increase in the thickness of neuroretinal rim. There was no significant change in the automatically detected BMO-Area (p = 0.32). The pressure-related increase in MRW correlated well with the postoperative IOP and cup-to-disc ratio (CDR). In regression analysis, the alteration in thickness of the neuroretinal rim could be well predicted in a model including CDR, change of IOP and mean deviation (MD) (R2 = 0.414, p < 0.001). RNFL showed a significant increase as well. Conclusion IOP-lowering surgery in patients with medically uncontrolled POAG causes an increased thickness of the SD-OCT derived ONH parameters. The changes of the RNFL after surgery showed no significant correlations with IOP changes. In contrast to this, highly significant correlations of MRW values with the IOP could be observed. The BMO-Area remained completely stable A preferred use of RNFL for follow-up should be discussed.
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Lazaridis G, Lorenzi M, Mohamed-Noriega J, Aguilar-Munoa S, Suzuki K, Nomoto H, Ourselin S, Garway-Heath DF, Crabb DP, Bunce C, Amalfitano F, Anand N, Azuara-Blanco A, Bourne RR, Broadway DC, Cunliffe IA, Diamond JP, Fraser SG, Ho TA, Martin KR, McNaught AI, Negi A, Shah A, Spry PG, White ET, Wormald RP, Xing W, Zeyen TG. OCT Signal Enhancement with Deep Learning. ACTA ACUST UNITED AC 2021; 4:295-304. [DOI: 10.1016/j.ogla.2020.10.008] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2020] [Revised: 10/04/2020] [Accepted: 10/06/2020] [Indexed: 01/29/2023]
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García G, Colomer A, Naranjo V. Glaucoma Detection from Raw SD-OCT Volumes: A Novel Approach Focused on Spatial Dependencies. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 200:105855. [PMID: 33303289 DOI: 10.1016/j.cmpb.2020.105855] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Accepted: 11/16/2020] [Indexed: 06/12/2023]
Abstract
BACKGROUND AND OBJECTIVE Glaucoma is the leading cause of blindness worldwide. Many studies based on fundus image and optical coherence tomography (OCT) imaging have been developed in the literature to help ophthalmologists through artificial-intelligence techniques. Currently, 3D spectral-domain optical coherence tomography (SD-OCT) samples have become more important since they could enclose promising information for glaucoma detection. To analyse the hidden knowledge of the 3D scans for glaucoma detection, we have proposed, for the first time, a deep-learning methodology based on leveraging the spatial dependencies of the features extracted from the B-scans. METHODS The experiments were performed on a database composed of 176 healthy and 144 glaucomatous SD-OCT volumes centred on the optic nerve head (ONH). The proposed methodology consists of two well-differentiated training stages: a slide-level feature extractor and a volume-based predictive model. The slide-level discriminator is characterised by two new, residual and attention, convolutional modules which are combined via skip-connections with other fine-tuned architectures. Regarding the second stage, we first carried out a data-volume conditioning before extracting the features from the slides of the SD-OCT volumes. Then, Long Short-Term Memory (LSTM) networks were used to combine the recurrent dependencies embedded in the latent space to provide a holistic feature vector, which was generated by the proposed sequential-weighting module (SWM). RESULTS The feature extractor reports AUC values higher than 0.93 both in the primary and external test sets. Otherwise, the proposed end-to-end system based on a combination of CNN and LSTM networks achieves an AUC of 0.8847 in the prediction stage, which outperforms other state-of-the-art approaches intended for glaucoma detection. Additionally, Class Activation Maps (CAMs) were computed to highlight the most interesting regions per B-scan when discerning between healthy and glaucomatous eyes from raw SD-OCT volumes. CONCLUSIONS The proposed model is able to extract the features from the B-scans of the volumes and combine the information of the latent space to perform a volume-level glaucoma prediction. Our model, which combines residual and attention blocks with a sequential weighting module to refine the LSTM outputs, surpass the results achieved from current state-of-the-art methods focused on 3D deep-learning architectures.
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Affiliation(s)
- Gabriel García
- Instituto de Investigación e Innovación en Bioingeniería (I3B), Universitat Politécnica de Valéncia (UPV), Valencia 46022, Spain.
| | - Adrián Colomer
- Instituto de Investigación e Innovación en Bioingeniería (I3B), Universitat Politécnica de Valéncia (UPV), Valencia 46022, Spain
| | - Valery Naranjo
- Instituto de Investigación e Innovación en Bioingeniería (I3B), Universitat Politécnica de Valéncia (UPV), Valencia 46022, Spain
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Local Glaucomatous Defects of the Circumpapillary Retinal Nerve Fiber Layer Show a Variety of Patterns of Progression. J Glaucoma 2021; 29:857-863. [PMID: 33003174 DOI: 10.1097/ijg.0000000000001620] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PRECIS The region of glaucomatous progression, seen on optical coherence tomography (OCT) images of the circumpapillary retinal nerve fiber layer (cRNFL), increases in width and depth in all eyes, but shows a variety of different patterns of loss across eyes. PURPOSE The purpose of this study was to examine the patterns of cRNFL loss secondary to glaucomatous progression in a region associated with the superior hemifield of the 24-2/30-2 visual field (VF). METHODS Twenty-four eyes (20 patients) with a diagnosis of glaucoma and evidence of progression on OCT had OCT disc cube scans on at least 3 separate visits (mean follow-up 7.4 y; range: 3.9 to 11.4). Circumpapillary b-scans were derived after enface images were aligned to assure that the study region (ie, 0 to -135 degrees, where 0 degree is 9 o'clock, on a right eye) coincided. Within this region, a region of progression (ROP) was defined based on the loss in cRNFL thickness between the first and subsequent visits. The width of the ROP was determined, along with the locations of its leading (close to fixation) and trailing edges. In addition, for each ROP, the location and depth at the point of maximal loss, total loss, and average remaining retinal nerve fiber layer were measured. RESULTS The ROP proceeded both toward and away from fixation. Across eyes, the ROP varied widely in width (32 to 131 degrees, mean 82.7 degrees), location, and loss at point of deepest loss (22 to 99 μm, mean 52.9 μm), as well as total cRNFL loss. CONCLUSIONS All eyes showed a widening and deepening of the ROP, but a variety of different patterns of progressive cRNFL loss. Thus, one should expect considerable variation in patterns of VF loss. Furthermore, conventional metrics (global or quadrant cRNFL thickness) do not fully depict the progressive changes that can be appreciated by inspecting OCT images.
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Global optical coherence tomography measures for detecting the progression of glaucoma have fundamental flaws. Eye (Lond) 2021; 35:2973-2982. [PMID: 33414534 PMCID: PMC8526823 DOI: 10.1038/s41433-020-01296-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 09/26/2020] [Accepted: 11/05/2020] [Indexed: 12/05/2022] Open
Abstract
Objective To understand the problems involved in using global OCT measures for detecting progression in early glaucoma. Subjects/Methods Eyes from 76 patients and 28 healthy controls (HC) had a least two OCT scans at least 1 year apart. To determine the 95% confidence intervals (CI), 151 eyes (49 HC and 102 patients) had at least two scans within 6 months. All eyes had 24-2 mean deviation ≥-6dB. The average (global) thicknesses of the circumpapillary retinal nerve fibre layer (cRNFL), GONH, and of the retinal ganglion cell layer plus inner plexiform layer (RGCLP), Gmac, were calculated. Using quantile regression, the 95% CI intervals were determined. Eyes outside the CIs were classified as “progressors.” For a reference standard (RS), four experts evaluated OCT and VF information. Results Compared to the RS, 31 of the 76 (40.8%) patient eyes were identified as progressors (RS-P), and 45 patient, and all 28 HC, eyes as nonprogressors (RS-NP). The metrics missed (false negative, FN) 15 (48%) (GONH) and 9 (29%) (Gmac) of the 31 RS-P. Further, GONH and/or Gmac falsely identified (false positive, FP) 10 (22.2%) of 45 patient RS-NP eyes and 7 (25%) of the 28 HC eyes as progressing. Post-hoc analysis identified three reasons (segmentation, centring, and local damage) for these errors. Conclusions Global metrics lead to FPs and FNs because of problems inherent in OCT scanning (segmentation and centring), and to FNs because they can miss local damage. These problems are difficult, if not impossible, to correct, and raise concerns about the advisability of using GONH and Gmac for detecting progression.
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Association Between Structure-function Characteristics and Visual Field Outcomes in Glaucoma Subjects With Intraocular Pressure Reduction After Trabeculectomy. J Glaucoma 2020; 29:648-655. [PMID: 32487949 DOI: 10.1097/ijg.0000000000001550] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
PRECIS Improvements in post-trabeculectomy visual field (VF) outcomes were found to be significantly associated with preoperative nerve fiber layer thickness parameters extracted from the sectorized structure-function relationship, baseline VF, and severity of glaucoma. OBJECTIVE To determine whether the preoperative structure-function relationship helps to predict visual outcomes at 1-year post-trabeculectomy. PATIENTS AND METHODS In total, 91 eyes from 87 participants who successfully underwent trabeculectomy were included in our study. All eyes received optical coherence tomography imaging and VF assessment using 30-2 standard automated perimetry preoperatively at baseline and postoperatively 1 year after trabeculectomy. The linear mixed-model analysis was used to assess the association of structure and function at baseline, and multivariate analysis to investigate factors associated with postoperative VF outcomes. RESULTS Results from multivariate and univariate analysis for VF 1 year after trabeculectomy showed that a positive preoperative retinal nerve fiber layer thickness deviation from the structure-function model was found to be significantly associated with improved postoperative VF outcomes [β=0.06 dB/μm; 95% confidence interval (CI), 0.03-0.09]. Other significant factors included baseline VF MD (β=-0.18; 95% CI, -0.23 to -0.13) and the presence of severe glaucoma (β=-1.69; 95% CI, -2.80 to -0.57). Intraocular pressure was positively associated with improved VF outcomes only in univariate analysis (β=0.06; 95% CI, 0.01-0.11). CONCLUSIONS AND RELEVANCE Characteristics derived from the baseline structure-function relationship were found to be strongly associated with postoperative VF outcomes. These findings suggest that the structure-function relationship could potentially have a role in predicting VF progression after trabeculectomy.
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Heikka T, Cense B, Jansonius NM. Retinal layer thicknesses retrieved with different segmentation algorithms from optical coherence tomography scans acquired under different signal-to-noise ratio conditions. BIOMEDICAL OPTICS EXPRESS 2020; 11:7079-7095. [PMID: 33408981 PMCID: PMC7747907 DOI: 10.1364/boe.399949] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 08/23/2020] [Accepted: 08/24/2020] [Indexed: 05/13/2023]
Abstract
Glaucomatous damage can be quantified by measuring the thickness of different retinal layers. However, poor image quality may hamper the accuracy of the layer thickness measurement. We determined the effect of poor image quality (low signal-to-noise ratio) on the different layer thicknesses and compared different segmentation algorithms regarding their robustness against this degrading effect. For this purpose, we performed OCT measurements in the macular area of healthy subjects and degraded the image quality by employing neutral density filters. We also analysed OCT scans from glaucoma patients with different disease severity. The algorithms used were: The Canon HS-100's built-in algorithm, DOCTRAP, IOWA, and FWHM, an approach we developed. We showed that the four algorithms used were all susceptible to noise at a varying degree, depending on the retinal layer assessed, and the results between different algorithms were not interchangeable. The algorithms also differed in their ability to differentiate between young healthy eyes and older glaucoma eyes and failed to accurately separate different glaucoma stages from each other.
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Affiliation(s)
- Tuomas Heikka
- Department of Ophthalmology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
| | - Barry Cense
- Department of Mechanical Engineering, Yonsei University, Seoul 03722, Republic of Korea
- Optical+Biomedical Engineering Laboratory, Department of Electrical, Electronic and Computer Engineering, University of Western Australia, Crawley, WA, Australia
| | - Nomdo M. Jansonius
- Department of Ophthalmology, University of Groningen, University Medical Center Groningen, Groningen, The Netherlands
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Desissaire S, Pollreisz A, Sedova A, Hajdu D, Datlinger F, Steiner S, Vass C, Schwarzhans F, Fischer G, Pircher M, Schmidt-Erfurth U, Hitzenberger CK. Analysis of retinal nerve fiber layer birefringence in patients with glaucoma and diabetic retinopathy by polarization sensitive OCT. BIOMEDICAL OPTICS EXPRESS 2020; 11:5488-5505. [PMID: 33149966 PMCID: PMC7587266 DOI: 10.1364/boe.402475] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 08/28/2020] [Accepted: 08/31/2020] [Indexed: 06/11/2023]
Abstract
The retinal nerve fiber layer (RNFL) is a fibrous tissue that shows form birefringence. This optical tissue property is related to the microstructure of the nerve fiber axons that carry electrical signals from the retina to the brain. Ocular diseases that are known to cause neurologic changes, like glaucoma or diabetic retinopathy (DR), might alter the birefringence of the RNFL, which could be used for diagnostic purposes. In this pilot study, we used a state-of-the-art polarization sensitive optical coherence tomography (PS-OCT) system with an integrated retinal tracker to analyze the RNFL birefringence in patients with glaucoma, DR, and in age-matched healthy controls. We recorded 3D PS-OCT raster scans of the optic nerve head area and high-quality averaged circumpapillary PS-OCT scans, from which RNFL thickness, retardation and birefringence were derived. The precision of birefringence measurements was 0.005°/µm. As compared to healthy controls, glaucoma patients showed a slightly reduced birefringence (0.129 vs. 0.135°/µm), although not statistically significant. The DR patients, however, showed a stronger reduction of RNFL birefringence (0.103 vs. 0.135°/µm) which was highly significant. This result might open new avenues into early diagnosis of DR and related neurologic changes.
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Affiliation(s)
- Sylvia Desissaire
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, 1090, Austria
| | - Andreas Pollreisz
- Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, 1090, Austria
| | - Aleksandra Sedova
- Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, 1090, Austria
| | - Dorottya Hajdu
- Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, 1090, Austria
| | - Felix Datlinger
- Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, 1090, Austria
| | - Stefan Steiner
- Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, 1090, Austria
| | - Clemens Vass
- Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, 1090, Austria
| | - Florian Schwarzhans
- Institute of Medical Information Management, Medical University of Vienna, Vienna, 1090, Austria
| | - Georg Fischer
- Institute of Medical Information Management, Medical University of Vienna, Vienna, 1090, Austria
| | - Michael Pircher
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, 1090, Austria
| | - Ursula Schmidt-Erfurth
- Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, 1090, Austria
| | - Christoph K. Hitzenberger
- Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, 1090, Austria
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Thompson AC, Jammal AA, Berchuck SI, Mariottoni EB, Wu Z, Daga FB, Ogata NG, Urata CN, Estrela T, Medeiros FA. Comparing the Rule of 5 to Trend-based Analysis for Detecting Glaucoma Progression on OCT. Ophthalmol Glaucoma 2020; 3:414-420. [PMID: 32723699 DOI: 10.1016/j.ogla.2020.06.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2020] [Revised: 06/03/2020] [Accepted: 06/08/2020] [Indexed: 11/24/2022]
Abstract
PURPOSE The rule of 5 is a simple rule for detecting retinal nerve fiber layer (RNFL) change on spectral-domain OCT (SD-OCT), in which a loss of 5 μm of global RNFL on a follow-up test is considered evidence of significant change when compared with the baseline. The rule is based on short-term test-retest variability of SD-OCT and is often used in clinical practice. The purpose of this study was to compare the rule of 5 with trend-based analysis of global RNFL thickness over time for detecting glaucomatous progression. DESIGN Prospective cohort. PARTICIPANTS A total of 300 eyes of 210 glaucoma subjects followed for an average of 5.4±1.5 years with a median of 11 (interquartile range, 7-14) visits. METHODS Trend-based analysis was performed by ordinary least-squares (OLS) linear regression of global RNFL thickness over time. For estimation of specificity, false-positives were obtained by assessing for progression on series of randomly permutated follow-up visits for each eye, which removes any systematic trend over time. The specificity of trend-based analysis was matched to that of the rule of 5 to allow meaningful comparison of the "hit rate," or the proportion of glaucoma eyes categorized as progressing at each time point, using the original sequence of visits. MAIN OUTCOME MEASURES Comparison between hit rates of trend-analysis versus rule of 5 at matched specificity. RESULTS After 5 years, the simple rule of 5 identified 37.5% of eyes as progressing at a specificity of 81.1%. At the same specificity, the hit rate for trend-based analysis was significantly greater than that of the rule of 5 (62.9% vs. 37.5%; P < 0.001). If the rule of 5 was required to be repeatable on a consecutive test, specificity improved to 93.4%, but hit rate decreased to 21.0%. At this higher specificity, trend-based analysis still had a significantly greater hit rate than the rule of 5 (47.4% vs. 21.0%, respectively; P < 0.001). CONCLUSIONS Trend-based analysis was superior to the simple rule of 5 for identifying progression in glaucoma eyes and should be preferred as a method for longitudinal assessment of global SD-OCT RNFL change over time.
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Affiliation(s)
- Atalie C Thompson
- Duke University Medical Center, Department of Ophthalmology, Durham, North Carolina
| | - Alessandro A Jammal
- Duke University Medical Center, Department of Ophthalmology, Durham, North Carolina
| | - Samuel I Berchuck
- Duke University Medical Center, Department of Ophthalmology, Durham, North Carolina; Duke FORGE, Durham, North Carolina
| | - Eduardo B Mariottoni
- Duke University Medical Center, Department of Ophthalmology, Durham, North Carolina
| | - Zhichao Wu
- Ophthalmology, Department of Surgery, The University of Melbourne, Melbourne, Australia
| | - Fabio B Daga
- Duke University Medical Center, Department of Ophthalmology, Durham, North Carolina
| | - Nara G Ogata
- Duke University Medical Center, Department of Ophthalmology, Durham, North Carolina
| | - Carla N Urata
- Duke University Medical Center, Department of Ophthalmology, Durham, North Carolina
| | - Tais Estrela
- Duke University Medical Center, Department of Ophthalmology, Durham, North Carolina
| | - Felipe A Medeiros
- Duke University Medical Center, Department of Ophthalmology, Durham, North Carolina.
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Towards multi-center glaucoma OCT image screening with semi-supervised joint structure and function multi-task learning. Med Image Anal 2020; 63:101695. [DOI: 10.1016/j.media.2020.101695] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2019] [Revised: 02/02/2020] [Accepted: 03/30/2020] [Indexed: 01/12/2023]
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Dhar SK, Raji K, Sandeep S, Abhijit. Study of correlation between stereopsis and retinal nerve fiber layer thickness in cases of glaucoma. Med J Armed Forces India 2020; 77:63-69. [PMID: 33487868 DOI: 10.1016/j.mjafi.2020.01.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2019] [Accepted: 01/08/2020] [Indexed: 11/24/2022] Open
Abstract
Background Glaucoma is an important and common optic neuropathy characterized by progressive loss of retinal ganglion cells and associated morphological changes to the optic nerve and retinal nerve fiber layer (RNFL). The most common assessment of visual function in glaucoma uses perimetric measurements of visual sensitivity. Only few studies have evaluated the binocular function in patients with glaucoma. This study was taken up to establish the correlation of RNFL thickness, in glaucoma, with near and distance stereopsis. Methods This pilot, cross-sectional observational study included 100 diagnosed cases of glaucoma and 100 normal participants as controls, studied over a period of one year. The records of all the participants were checked, and only established cases of glaucoma after fulfilling the inclusion and exclusion criteria were included. Analysis of the RNFL using spectral-domain optical coherence tomography was carried out. All the participants were thereafter evaluated for stereoacuity by near (at 40 inches) and distance (at 3 meter) Randot stereoacuity charts. Results There was a negative correlation between the RNFL thickness and the absolute value of streoacuity (-0.303 for distance versus -0.101 for near in cases and -0.308 for distance and -0.416 for near in control group), decreasing the actual functional stereoacuity, therefore the cases with lower RNFL thickness had lower stereoacuity both for distance and near, however it was statistically significant only for distance (p=0.002). Conclusion Functional aspects, such as stereoacuity, may also be affected in the glaucoma because of decrease in RNFL thickness. Therefore, binocular status should also be evaluated in cases of glaucoma.
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Affiliation(s)
- Sanjay Kumar Dhar
- Classified Specialist (Ophthalmology/ Paediatric Ophthalmology & Squint), Army Hospital (R&R), Delhi Cantt, 110010, India
| | - K Raji
- Senior Advisor, (Ophthalmology & VR Surgery), Army Hospital (R&R), Delhi Cantt, 110010, India
| | - Shankar Sandeep
- Commandant, Military Hospital Wellington, Tamil Nadu, 643231, India
| | - Abhijit
- Fellow, Community Ophthalmology, HV Desai Eye Hospital, Pune, 411060, India
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Russakoff DB, Mannil SS, Oakley JD, Ran AR, Cheung CY, Dasari S, Riyazzuddin M, Nagaraj S, Rao HL, Chang D, Chang RT. A 3D Deep Learning System for Detecting Referable Glaucoma Using Full OCT Macular Cube Scans. Transl Vis Sci Technol 2020; 9:12. [PMID: 32704418 PMCID: PMC7347026 DOI: 10.1167/tvst.9.2.12] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Accepted: 12/10/2019] [Indexed: 11/24/2022] Open
Abstract
Purpose The purpose of this study was to develop a 3D deep learning system from spectral domain optical coherence tomography (SD-OCT) macular cubes to differentiate between referable and nonreferable cases for glaucoma applied to real-world datasets to understand how this would affect the performance. Methods There were 2805 Cirrus optical coherence tomography (OCT) macula volumes (Macula protocol 512 × 128) of 1095 eyes from 586 patients at a single site that were used to train a fully 3D convolutional neural network (CNN). Referable glaucoma included true glaucoma, pre-perimetric glaucoma, and high-risk suspects, based on qualitative fundus photographs, visual fields, OCT reports, and clinical examinations, including intraocular pressure (IOP) and treatment history as the binary (two class) ground truth. The curated real-world dataset did not include eyes with retinal disease or nonglaucomatous optic neuropathies. The cubes were first homogenized using layer segmentation with the Orion Software (Voxeleron) to achieve standardization. The algorithm was tested on two separate external validation sets from different glaucoma studies, comprised of Cirrus macular cube scans of 505 and 336 eyes, respectively. Results The area under the receiver operating characteristic (AUROC) curve for the development dataset for distinguishing referable glaucoma was 0.88 for our CNN using homogenization, 0.82 without homogenization, and 0.81 for a CNN architecture from the existing literature. For the external validation datasets, which had different glaucoma definitions, the AUCs were 0.78 and 0.95, respectively. The performance of the model across myopia severity distribution has been assessed in the dataset from the United States and was found to have an AUC of 0.85, 0.92, and 0.95 in the severe, moderate, and mild myopia, respectively. Conclusions A 3D deep learning algorithm trained on macular OCT volumes without retinal disease to detect referable glaucoma performs better with retinal segmentation preprocessing and performs reasonably well across all levels of myopia. Translational Relevance Interpretation of OCT macula volumes based on normative data color distributions is highly influenced by population demographics and characteristics, such as refractive error, as well as the size of the normative database. Referable glaucoma, in this study, was chosen to include cases that should be seen by a specialist. This study is unique because it uses multimodal patient data for the glaucoma definition, and includes all severities of myopia as well as validates the algorithm with international data to understand generalizability potential.
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Affiliation(s)
| | - Suria S. Mannil
- Byers Eye Institute, Stanford University, Palo Alto, CA, USA
| | | | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong
| | - Carol Y. Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong
| | | | | | | | | | - Dolly Chang
- Byers Eye Institute, Stanford University, Palo Alto, CA, USA
| | - Robert T. Chang
- Byers Eye Institute, Stanford University, Palo Alto, CA, USA
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Mohammadzadeh V, Rabiolo A, Fu Q, Morales E, Coleman AL, Law SK, Caprioli J, Nouri-Mahdavi K. Longitudinal Macular Structure-Function Relationships in Glaucoma. Ophthalmology 2020; 127:888-900. [PMID: 32173112 DOI: 10.1016/j.ophtha.2020.01.023] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2019] [Revised: 01/08/2020] [Accepted: 01/10/2020] [Indexed: 11/30/2022] Open
Abstract
PURPOSE To investigate the relationship between longitudinal changes in macular thickness measurements from OCT and changes in central visual field (VF) in patients with glaucoma with central or advanced damage at baseline. DESIGN Longitudinal cohort study. PARTICIPANTS A total of 116 eyes with ≥3 years of follow-up and ≥5 macular OCT images and central 10° VF tests were selected. METHODS OCT superpixels and VF locations were matched correcting for retinal ganglion cell (RGC) displacement. Superpixel thickness and VF total deviation (TD) values, in both logarithmic and linear scales, were averaged within 3 eccentricities (3.4°, 5.6°, and 6.8°) and superior and inferior hemiretinas and hemifields. We estimated pointwise TD rates of change and rates of change at superpixels for full macular thickness (FMT), ganglion cell complex (GCC), ganglion cell inner plexiform layer (GCIPL), and ganglion cell layer (GCL). Correlation of structure-function (SF) rates of change was investigated with parametric tests. We compared the proportion of worsening and positive slopes for superpixels and VF test locations (negative vs. positive rates of change with P < 0.05) throughout the follow-up period. Permutation analyses were used to control specificity. MAIN OUTCOME MEASURES Magnitude of correlation between structural and functional rates of change and proportion of worsening and positive slopes as a function of follow-up time. RESULTS The median (interquartile range) follow-up and number of exams were 4.2 (3.7-4.6) years and 8 (7-9), respectively. The highest correlation of change rates was observed at 3.4° and 5.6° eccentricities (r = 0.24, 0.41, 0.40, and 0.40 for FMT, GCC, GCIPL, and GCL for 3.4° eccentricity and r = 0.28, 0.32, 0.31, and 0.32 for FMT, GCC, GCIPL, and GCL for 5.6° eccentricity, respectively). Although GCC measures demonstrated the highest overall longitudinal SF correlations, the differences were not statistically significant. Significant structural worsening was more frequently detected than functional deterioration at 3- and 5-year time points (P < 0.025). Permutation analyses also confirmed this finding. CONCLUSIONS Correlations between central structural and functional rates of change were weak to fair in this cohort. Structural changes were detected more frequently than functional changes. Measurements of both structure and function are required for optimal detection of central progression.
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Affiliation(s)
- Vahid Mohammadzadeh
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Alessandro Rabiolo
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California; Department of Ophthalmology, University Vita-Salute, IRCCS San Raffaele, Milan, Italy
| | - Qiang Fu
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California; Department of Ophthalmology, The First Affiliated Hospital, Qiqihar Medical University, Qiqihar, China
| | - Esteban Morales
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Anne L Coleman
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Simon K Law
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Joseph Caprioli
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California
| | - Kouros Nouri-Mahdavi
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California.
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A meta-analysis to study the effect of pan retinal photocoagulation on retinal nerve fiber layer thickness in diabetic retinopathy patients. Rom J Ophthalmol 2020; 64:8-14. [PMID: 32292851 PMCID: PMC7141917] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022] Open
Abstract
Background. Diabetic retinopathy is a microvascular disease, it is associated with changes in peripapillary retinal nerve fiber layer thickness, these changes being more pronounced in PDR (Proliferative diabetic retinopathy) patients undergoing laser photocoagulation. Objective. To assess changes in peripapillary retinal nerve fiber layer thickness in proliferative diabetic retinopathy patients using optical coherence tomogram (OCT). Methods. The database search was conducted in June 2018 and continued until October 2018. The search engines used included Pubmed, Medline, OVID and Google Scholar. A meta-analysis of weighted mean difference and standard deviation was conducted. Results. A total of 10 studies containing 377 eyes of PDR patients were selected. The analysis of the included studies revealed no significant effect of PRP on average retinal nerve fiber layer thickness (0.249, 95% CI: -0.985 to 1.483) using OCT. Conclusion. Hence, to conclude, our meta-analysis revealed that there was no significant effect of PRP on RNFL thickness and the impact of PRP could vary. Measurement of peripapillary RNFL thickness may yield erroneous and unpredictable results in this subgroup of patients, further confounding the evaluation of nerve fiber layer damage and its progression.
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39
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Lee JS, Seong GJ, Kim CY, Lee SY, Bae HW. Risk factors associated with progressive nerve fiber layer thinning in open-angle glaucoma with mean intraocular pressure below 15 mmHg. Sci Rep 2019; 9:19811. [PMID: 31875007 PMCID: PMC6930196 DOI: 10.1038/s41598-019-56387-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 12/11/2019] [Indexed: 11/12/2022] Open
Abstract
The purpose of this study was to identify risk factors associated with progressive retinal nerve fiber layer(RNFL) thinning of open-angle glaucoma(OAG) in patients whose intraocular pressure(IOP) was maintained low with medical treatment. Based on a retrospective review of medical records, OAG patients with ≥60 months of follow-up and mean IOP below 15 mmHg were recruited. All eyes underwent IOP measurement with Goldmann applanation tonometer(GAT), standard automated perimetry(SAP), and cirrus optical coherence tomography(cirrus OCT) at 6 month or 1 year intervals. RNFL thinning was assessed using the Guided Progression Analysis(GPA) software. Forty-one eyes of 41 patients (mean age 54.9 ± 13.5) were followed up for 77.8 ± 7.8 months. GPA detected 20 eyes (48.8%) with progressive RNFL thinning(−1.5 ± 0.5 um/year), who were subsequently classified as the ‘rapid progression group.’ Those whose rate of change in RNFL thickness was slower than −1.00 µm/year was classified as the ‘slow progression group’ (n = 21, −0.0 ± 0.4 um/year, P < 0.001). Mean IOP after initiating therapy was 13.2 ± 1.1 mmHg in the rapid progression group and 13.1 ± 1.3 mmHg in the slow progression group (P = 0.300; 14.8 ± 10.0% vs. 19.6 ± 12.4% reduction, P = 0.155). Disc hemorrhage was found to more frequently occur in the rapid progression group (P = 0.001). Multivariate logistic regression analysis showed that patients with disc hemorrhage were at a higher risk for progressive RNFL thinning in OAG (OR 37.529 95% CI 2.915–483.140) after adjusting for baseline co-variates (P = 0.005). In conclusion, disc hemorrhage is associated with progressive RNFL thinning in OAG with well-maintained IOP. Factors other than IOP appear to also play a role in OAG progression.
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Affiliation(s)
- Jihei Sara Lee
- Department of Ophthalmology, Severance Hospital, Institute of Vision Research, Seoul, Korea
| | - Gong Je Seong
- Department of Ophthalmology, Severance Hospital, Institute of Vision Research, Seoul, Korea
| | - Chan Yun Kim
- Department of Ophthalmology, Severance Hospital, Institute of Vision Research, Seoul, Korea
| | - Sang Yeop Lee
- Department of Ophthalmology, Severance Hospital, Institute of Vision Research, Seoul, Korea
| | - Hyoung Won Bae
- Department of Ophthalmology, Severance Hospital, Institute of Vision Research, Seoul, Korea.
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40
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Lee WJ, Kim TJ, Kim YK, Jeoung JW, Park KH. Serial Combined Wide-Field Optical Coherence Tomography Maps for Detection of Early Glaucomatous Structural Progression. JAMA Ophthalmol 2019; 136:1121-1127. [PMID: 30054615 DOI: 10.1001/jamaophthalmol.2018.3160] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Importance Both parapapillary and macular areas are important in determining the progression of early glaucoma. However, no attempt has been made to assess the progression of glaucoma in images that combine the 2 areas. Objective To evaluate the potential usefulness of serial analysis of combined wide-field optical coherence tomography (OCT) maps for detection of structural progression in patients with early glaucoma. Design, Setting, and Participants Retrospective observational study. Patients with early primary open-angle glaucoma with a minimum of 3-year follow-up involving serial spectral-domain OCT measurement were analyzed. Patients were divided into a nonprogressor group (n = 47) and a progressor group (n = 47) on the basis of serial stereo disc photography and red-free photography. Serial combined wide-field OCT maps integrating parapapillary retinal nerve fiber layer (RNFL) and macular ganglion cell-inner plexiform layer (GCIPL) maps were generated with the embedded software of serial spectral-domain OCT. Glaucoma specialists then assessed the structural progression detection ability of those serial wide-field OCT maps for early glaucomatous eyes and compared their sensitivity with those of RNFL and GCIPL guided progression analyses (GPAs). Main Outcomes and Measures The diagnostic ability of the serial wide-field OCT maps for early glaucomatous structural progression. Results Ninety-four patients (mean [SD] age, 51.4 [12.3] years; 48 [51.1%] women; all Korean) were included. The serial wide-field OCT map analysis showed good agreement for detection of structural progression between the 2 glaucoma graders (wide-field OCT thickness map: κ = 0.649; wide-field OCT deviation map: κ = 0.833). These maps showed early glaucomatous structural progression detection abilities comparable with those of RNFL and GCIPL GPAs (sensitivities of wide-field OCT thickness map, wide-field OCT deviation map, RNFL GPA, and GCIPL GPA = 63.8%, 83.0%, 83.0%, and 66.0%, respectively, all P > .05; specificities of wide-field OCT thickness map, wide-field OCT deviation map, RNFL GPA, and GCIPL GPA = 93.6%, 95.7%, 84.8%, and 93.6%, respectively, all P > .05). Conclusions and Relevance The serial combined wide-field OCT maps integrating RNFL and GCIPL maps performed well in detecting structural progression in early glaucomatous eyes. Confirmation in an independent prospective study might provide greater confidence in this conclusion.
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Affiliation(s)
- Won June Lee
- Department of Ophthalmology, Hanyang University Hospital, Seoul, Korea.,Department of Ophthalmology, Hanyang University College of Medicine, Seoul, Korea
| | | | - Young Kook Kim
- Department of Ophthalmology, Seoul National University Hospital, Seoul, Korea.,Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea
| | - Jin Wook Jeoung
- Department of Ophthalmology, Seoul National University Hospital, Seoul, Korea.,Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea
| | - Ki Ho Park
- Department of Ophthalmology, Seoul National University Hospital, Seoul, Korea.,Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea
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He Y, Carass A, Liu Y, Jedynak BM, Solomon SD, Saidha S, Calabresi PA, Prince JL. Fully Convolutional Boundary Regression for Retina OCT Segmentation. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2019; 11764:120-128. [PMID: 31853524 DOI: 10.1007/978-3-030-32239-7_14] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
A major goal of analyzing retinal optical coherence tomography (OCT) images is retinal layer segmentation. Accurate automated algorithms for segmenting smooth continuous layer surfaces, with correct hierarchy (topology) are desired for monitoring disease progression. State-of-the-art methods use a trained classifier to label each pixel into background, layer, or surface pixels. The final step of extracting the desired smooth surfaces with correct topology are mostly performed by graph methods (e.g. shortest path, graph cut). However, manually building a graph with varying constraints by retinal region and pathology and solving the minimization with specialized algorithms will degrade the flexibility and time efficiency of the whole framework. In this paper, we directly model the distribution of surface positions using a deep network with a fully differentiable soft argmax to obtain smooth, continuous surfaces in a single feed forward operation. A special topology module is used in the deep network both in the training and testing stages to guarantee the surface topology. An extra deep network output branch is also used for predicting lesion and layers in a pixel-wise labeling scheme. The proposed method was evaluated on two publicly available data sets of healthy controls, subjects with multiple sclerosis, and diabetic macular edema; it achieves state-of-the art sub-pixel results.
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Affiliation(s)
- Yufan He
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.,Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Yihao Liu
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Bruno M Jedynak
- Department of Mathematics and Statistics, Portland State University, Portland, OR 97201, USA
| | - Sharon D Solomon
- Wilmer Eye Institute, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Shiv Saidha
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Peter A Calabresi
- Department of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.,Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
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42
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He Y, Carass A, Liu Y, Jedynak BM, Solomon SD, Saidha S, Calabresi PA, Prince JL. Deep learning based topology guaranteed surface and MME segmentation of multiple sclerosis subjects from retinal OCT. BIOMEDICAL OPTICS EXPRESS 2019; 10:5042-5058. [PMID: 31646029 PMCID: PMC6788619 DOI: 10.1364/boe.10.005042] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Revised: 09/01/2019] [Accepted: 09/02/2019] [Indexed: 05/11/2023]
Abstract
Optical coherence tomography (OCT) is a noninvasive imaging modality that can be used to obtain depth images of the retina. Patients with multiple sclerosis (MS) have thinning retinal nerve fiber and ganglion cell layers, and approximately 5% of MS patients will develop microcystic macular edema (MME) within the retina. Segmentation of both the retinal layers and MME can provide important information to help monitor MS progression. Graph-based segmentation with machine learning preprocessing is the leading method for retinal layer segmentation, providing accurate surface delineations with the correct topological ordering. However, graph methods are time-consuming and they do not optimally incorporate joint MME segmentation. This paper presents a deep network that extracts continuous, smooth, and topology-guaranteed surfaces and MMEs. The network learns shape priors automatically during training rather than being hard-coded as in graph methods. In this new approach, retinal surfaces and MMEs are segmented together with two cascaded deep networks in a single feed forward propagation. The proposed framework obtains retinal surfaces (separating the layers) with sub-pixel surface accuracy comparable to the best existing graph methods and MMEs with better accuracy than the state-of-the-art method. The full segmentation operation takes only ten seconds for a 3D volume.
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Affiliation(s)
- Yufan He
- Deptartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Aaron Carass
- Deptartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Yihao Liu
- Deptartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Bruno M. Jedynak
- Department of Mathematics & Statistics, Portland State University, Portland, OR 97201, USA
| | - Sharon D. Solomon
- Wilmer Eye Institute, The Johns Hopkins University School of Medicine, MD 21287, USA
| | - Shiv Saidha
- Department of Neurology, The Johns Hopkins University School of Medicine, MD 21287, USA
| | - Peter A. Calabresi
- Department of Neurology, The Johns Hopkins University School of Medicine, MD 21287, USA
| | - Jerry L. Prince
- Deptartment of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
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Wadhwani M, Bali S, Bhartiya S, Mahabir M, Upadhaya A, Dada T, Sharma A, Mishra SK. Long term effect of panretinal photocoagulation on retinal nerve fiber layer parameters in patients with proliferative diabetic retinopathy. Oman J Ophthalmol 2019; 12:181-185. [PMID: 31902994 PMCID: PMC6826597 DOI: 10.4103/ojo.ojo_39_2018] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
PURPOSE: This study aimed to evaluate the long-term effect of panretinal photocoagulation (PRP) on the retinal nerve fiber layer (RNFL) in patients with proliferative diabetic retinopathy (PDR). METHODS: This was a prospective longitudinal cohort study examining 42 eyes of 42 patients with PDR undergoing PRP. Peripapillary RNFL thickness (RNFLT) was measured using spectral-domain optical coherence tomography at baseline, 1 year, and 3 years following PRP. RESULTS: The mean “average RNFLT” was 89.88 ± 14.26 μm at baseline, 85.75 ± 11.36 μm at 1-year follow-up, and 83.33 ± 11.96 μm at 3-year follow-up. There was a statistically significant difference in the average RNFL thickness at baseline and 1 year and 3 years after PRP. At 1-year follow-up, superior, inferior, and nasal RNFL measurements reduced significantly from baseline (P < 0.01). The reduction in RNFL remained statistically significant for superior and inferior quadrants 3 years after PRP. CONCLUSION: PRP causes a reduction in RNFL thickness until 3 years after the procedure. Caution should be exercised while interpreting peripapillary RNFL thickness scans in patients who have undergone PRP for diabetic retinopathy.
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Affiliation(s)
- Meenakshi Wadhwani
- Department of Community Ophthalmology, Dr. Rajendra Prasad Centre For Ophthalmic Sciences, AIIMS, New Delhi, India
| | - Shweta Bali
- Department of Ophthalmology, University of Otawa, Canada
| | | | - Manish Mahabir
- Retina Unit, Dr. Rajendra Prasad Centre For Ophthalmic Sciences, AIIMS, New Delhi, India
| | - Ashish Upadhaya
- Department of Biostatics, Dr. Rajendra Prasad Centre For Ophthalmic Sciences, AIIMS, New Delhi, India
| | - Tanuj Dada
- Glaucoma Unit, Dr. Rajendra Prasad Centre For Ophthalmic Sciences, AIIMS, New Delhi, India
| | - Anu Sharma
- Retina Lab, Dr. Rajendra Prasad Centre For Ophthalmic Sciences, AIIMS, New Delhi, India
| | - Sanjay Kumar Mishra
- Department of Ophthalmology, Command Hospital (Central Command), Lucknow, Uttar Pradesh, India
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Maetschke S, Antony B, Ishikawa H, Wollstein G, Schuman J, Garnavi R. A feature agnostic approach for glaucoma detection in OCT volumes. PLoS One 2019; 14:e0219126. [PMID: 31260494 PMCID: PMC6602191 DOI: 10.1371/journal.pone.0219126] [Citation(s) in RCA: 100] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2018] [Accepted: 06/17/2019] [Indexed: 01/16/2023] Open
Abstract
Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCIPL) are commonly employed for the diagnosis and monitoring of glaucoma. Previously, machine learning techniques have relied on segmentation-based imaging features such as the peripapillary RNFL thickness and the cup-to-disc ratio. Here, we propose a deep learning technique that classifies eyes as healthy or glaucomatous directly from raw, unsegmented OCT volumes of the optic nerve head (ONH) using a 3D Convolutional Neural Network (CNN). We compared the accuracy of this technique with various feature-based machine learning algorithms and demonstrated the superiority of the proposed deep learning based method. Logistic regression was found to be the best performing classical machine learning technique with an AUC of 0.89. In direct comparison, the deep learning approach achieved a substantially higher AUC of 0.94 with the additional advantage of providing insight into which regions of an OCT volume are important for glaucoma detection. Computing Class Activation Maps (CAM), we found that the CNN identified neuroretinal rim and optic disc cupping as well as the lamina cribrosa (LC) and its surrounding areas as the regions significantly associated with the glaucoma classification. These regions anatomically correspond to the well established and commonly used clinical markers for glaucoma diagnosis such as increased cup volume, cup diameter, and neuroretinal rim thinning at the superior and inferior segments.
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Affiliation(s)
| | | | - Hiroshi Ishikawa
- NYU Langone Eye Center, New York University School of Medicine, New York, NY, United States of America
| | - Gadi Wollstein
- NYU Langone Eye Center, New York University School of Medicine, New York, NY, United States of America
| | - Joel Schuman
- NYU Langone Eye Center, New York University School of Medicine, New York, NY, United States of America
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Wang J, Wang Z, Li F, Qu G, Qiao Y, Lv H, Zhang X. Joint retina segmentation and classification for early glaucoma diagnosis. BIOMEDICAL OPTICS EXPRESS 2019; 10:2639-2656. [PMID: 31149385 PMCID: PMC6524599 DOI: 10.1364/boe.10.002639] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2018] [Revised: 02/22/2019] [Accepted: 02/23/2019] [Indexed: 05/17/2023]
Abstract
We propose a joint segmentation and classification deep model for early glaucoma diagnosis using retina imaging with optical coherence tomography (OCT). Our motivation roots in the observation that ophthalmologists make the clinical decision by analyzing the retinal nerve fiber layer (RNFL) from OCT images. To simulate this process, we propose a novel deep model that joins the retinal layer segmentation and glaucoma classification. Our model consists of three parts. First, the segmentation network simultaneously predicts both six retinal layers and five boundaries between them. Then, we introduce a post processing algorithm to fuse the two results while enforcing the topology correctness. Finally, the classification network takes the RNFL thickness vector as input and outputs the probability of being glaucoma. In the classification network, we propose a carefully designed module to implement the clinical strategy to diagnose glaucoma. We validate our method both in a collected dataset of 1004 circular OCT B-Scans from 234 subjects and in a public dataset of 110 B-Scans from 10 patients with diabetic macular edema. Experimental results demonstrate that our method achieves superior segmentation performance than other state-of-the-art methods both in our collected dataset and in public dataset with severe retina pathology. For glaucoma classification, our model achieves diagnostic accuracy of 81.4% with AUC of 0.864, which clearly outperforms baseline methods.
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Affiliation(s)
- Jie Wang
- Department of Automation, Tsinghua University, Beijing,
China
| | - Zhe Wang
- SenseTime Group Limited, Beijing,
China
| | - Fei Li
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou,
China
| | - Guoxiang Qu
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen,
China
| | - Yu Qiao
- Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen,
China
| | - Hairong Lv
- Department of Automation, Tsinghua University, Beijing,
China
| | - Xiulan Zhang
- Zhongshan Ophthalmic Center, State Key Laboratory of Ophthalmology, Sun Yat-Sen University, Guangzhou,
China
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46
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Lee WJ, Baek SU, Kim YK, Park KH, Jeoung JW. Rates of Ganglion Cell-Inner Plexiform Layer Thinning in Normal, Open-Angle Glaucoma and Pseudoexfoliation Glaucoma Eyes: A Trend-Based Analysis. ACTA ACUST UNITED AC 2019; 60:599-604. [DOI: 10.1167/iovs.18-25296] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Affiliation(s)
- Won June Lee
- Department of Ophthalmology, Hanyang University College of Medicine, Seoul, Korea
- Department of Ophthalmology, Hanyang University Hospital, Seoul, Korea
| | - Sung Uk Baek
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea
- Department of Ophthalmology, Seoul National University Hospital, Seoul, Korea
| | - Young Kook Kim
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea
- Department of Ophthalmology, Seoul National University Hospital, Seoul, Korea
| | - Ki Ho Park
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea
- Department of Ophthalmology, Seoul National University Hospital, Seoul, Korea
| | - Jin Wook Jeoung
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea
- Department of Ophthalmology, Seoul National University Hospital, Seoul, Korea
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47
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Zha Y, Huang W, Zhuang J, Cai J. Posterior pole asymmetry analysis and retinal nerve fibre layer thickness measurements in primary angle-closure suspect patients. BMC Ophthalmol 2019; 19:36. [PMID: 30691419 PMCID: PMC6350334 DOI: 10.1186/s12886-019-1034-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2018] [Accepted: 01/08/2019] [Indexed: 11/15/2022] Open
Abstract
Purpose To measure peripapillary retinal nerve fiber layer (RNFL) thickness and posterior pole retinal thickness in primary angle-closure suspects (PACS) by Spectral domain optical coherence tomography (SD-OCT) and to be compared with normal subjects. Methods Thirty five primary angle-closure suspect patients and thirty normal subjects were enrolled in this study. Peripapillary RNFL and posterior pole retinal thickness by posterior pole asymmetry analysis (PPAA) in SD-OCT were measured. Results No significant difference was found in both groups on age, sex distribution, refractive error, intraocular pressure (IOP) and axial length. The PACS group exhibited significantly thinner macular retinal thickness and larger asymmetry on posterior pole region compared with the control group. Yet no significant difference of peripapillary RNFL parameters was found between PACS group and normal control group. A negative correlation was observed between the total retinal thickness on posterior pole region and age when all the PACS participants were analyzed. Conclusions Posterior pole retinal thickness measurements obtained by Heidelberg Spectralis SD-OCT using PPAA showed significant thinner change in PACS group than healthy controls. Only age seemed to be an indicator in the occurrence of glaucomatous damage in PACS patients.
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Affiliation(s)
- Yi Zha
- Department of Ophthalmology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Wei Huang
- Department of Ophthalmology, The first People's Hospital of Shaoyang, Shaoyang, 422000, Hunan, China
| | - Jinfei Zhuang
- Department of Ophthalmology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China
| | - Jianqiu Cai
- Department of Ophthalmology, The Second Affiliated Hospital and Yuying Children's Hospital of Wenzhou Medical University, Wenzhou, 325027, Zhejiang, China.
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Abstract
BACKGROUND Structural endpoints have been discussed as surrogate endpoints for the approval of neuroprotective drugs in glaucoma. OBJECTIVE Is the evidence strong enough to establish structural endpoints as surrogate endpoints? MATERIAL AND METHODS Review of current understanding between structure and function in glaucoma. RESULTS The introduction of optical coherence tomography has revolutionized imaging in glaucoma patients. Clinically either the nerve fiber layer thickness can be measured along a circle centered in the optic nerve head or the ganglion cell layer thickness can be assessed in the macular region, the latter being quantified in combination with other inner retinal layers. On a microscopic level there is a strong correlation between structural and functional loss but this relation can only partially be described with currently available clinical methods. This is particularly true for longitudinal course of the disease in glaucoma patients. Novel imaging techniques that are not yet used clinically may have the potential to increase our understanding between structure and function in glaucoma but further research in this field is required. CONCLUSION The current evidence does not allow the establishment of structural endpoints as surrogate endpoints for phase 3 studies in glaucoma. Neuroprotective drugs have to be approved on the basis of visual field data because this is the patient-relevant endpoint. Structural endpoints can, however, play an important role in phase 2 and proof of concept studies.
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Affiliation(s)
- A Popa-Cherechenau
- Universitätsklinik für Klinische Pharmakologie, Medizinische Universität Wien, Wien, Österreich
- Medizinische und Pharmazeutische Universität Carol Davila, Bukarest, Rumänien
- Abteilung für Ophthalmologie, Notfallzentrum der Universitätsklinik Bukarest, Bukarest, Rumänien
| | - D Schmidl
- Universitätsklinik für Klinische Pharmakologie, Medizinische Universität Wien, Wien, Österreich
| | - G Garhöfer
- Universitätsklinik für Klinische Pharmakologie, Medizinische Universität Wien, Wien, Österreich
| | - L Schmetterer
- Universitätsklinik für Klinische Pharmakologie, Medizinische Universität Wien, Wien, Österreich.
- Singapore Eye Research Institute, SERI (Augenforschungszentrum Singapur), College Str. 20, Discovery Tower Ebene 6, 169856, Singapur, Singapur.
- Lee Kong Chian Medical Schools, Nanyang Technological University (NTU), Singapur, Singapur.
- Klinisches Fortbildungszentrum Ophthalmologie und Visual Sciences, Duke-NUS Medical School, Singapur, Singapur.
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapur, Singapur.
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Ledolter J, Kardon RH. Assessing Trends in Functional and Structural Characteristics: A Survey of Statistical Methods With an Example From Ophthalmology. Transl Vis Sci Technol 2018; 7:34. [PMID: 30402341 PMCID: PMC6213778 DOI: 10.1167/tvst.7.5.34] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2018] [Accepted: 09/08/2018] [Indexed: 11/24/2022] Open
Abstract
Purpose Clinical decisions on treatment are usually based on short-term records of consecutive measurements of structure and function. Useful models for analyzing average trends and a description of statistical methods for classifying individual subjects on the basis of subject-specific trend progressions are presented. Methods Random effects trend models allow intercepts and slopes of the trend regression to vary across subjects around group-specific mean intercepts and mean slopes. Model results assess whether average intercepts and slopes and subject variability in intercepts and slopes are the same across groups. Fisher's discriminant functions are used for classification. Results Methods are presented and illustrated on structural visual data from a multiyear perimetry study. Average thickness of the ganglion cell layer from the optical coherence tomography macula scan and of the retinal nerve fiber layer from the optic disc scan for both glaucoma patients on optimal treatment and normal subjects are analyzed. The random effects trend model shows that average intercepts of glaucoma patients and normal subjects are quite different, but that average slopes are the same, and that the subject variability in both intercepts and slopes is larger for the glaucoma group. These findings explain why the subject-specific trend progression is not a good classifier; it is the level of the measurement (intercept or baseline value) that carries useful information in this particular cohort example. Translational Relevance Clinicians base decisions on short-term records of consecutive measurements and need simple statistical tools to analyze the information. This paper discusses useful methods for analyzing short time series data. Model results assess whether there exist significant trends and whether average trends are different across groups. The paper discusses whether clinical measures classify patients reliably into disease groups, given their variability. With ever more available data, classification plays a central role of personalized medicine.
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Affiliation(s)
- Johannes Ledolter
- Department of Management Sciences at the University of Iowa, Iowa City, IA, USA.,Iowa City VA Medical Center, Iowa City, IA, USA
| | - Randy H Kardon
- Iowa City VA Medical Center, Iowa City, IA, USA.,Department of Ophthalmology and Visual Sciences at the University of Iowa Hospital, Iowa City, IA, USA
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Lin PW, Chang HW, Lin JP, Lai IC. Analysis of peripapillary retinal nerve fiber layer and inner macular layers by spectral-domain optical coherence tomography for detection of early glaucoma. Int J Ophthalmol 2018; 11:1163-1172. [PMID: 30046534 DOI: 10.18240/ijo.2018.07.15] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2017] [Accepted: 09/28/2017] [Indexed: 12/30/2022] Open
Abstract
AIM To analyze the diagnostic capabilities of peripapillary retinal nerve fiber layer (pRNFL) thickness and segmented inner macular layer (IML) thickness measured by spectral-domain optical coherence tomography for detection of early glaucoma. METHODS Fifty-three patients with primary open angle glaucoma (POAG), 60 patients with normal tension glaucoma (NTG) and 32 normal control subjects were enrolled. Thicknesses of pRNFL, total macular layers (TML), and the IML, including macular RNFL (mRNFL) and macular ganglion cell layer (mGCL) were assessed. The areas under the receiver operating characteristic curves (AROC) were calculated to compare the diagnostic power of different parameters. RESULTS There were no differences in the parameters of pRNFL, TML, and IML between POAG and NTG groups. The thicknesses of superior and inferior mGCL showed significant correlation with mean deviation of visual field (R2=0.071, P=0.004; R2=0.08, P=0.002). The mGCL thickness significantly correlated with the pRNFL thickness in the superior and inferior quadrants (R2=0.156, P<0.001; R2=0.407, P<0.001). The thickness of the inferior-outer sector of macula had greater AROCs than those in the inferior-inner sector of macula. The AROCs for superior (0.894) and inferior (0.879) pRNFL thicknesses were similar with the AROCs for superior (0.839) and inferior mGCL (0.864) thicknesses. Sensitivities at 80% specificity for global pRNFL, inferior-outer mGCL and inferior-outer mRNFL thicknesses were 0.938, 0.867, and 0.725, respectively. CONCLUSION The diagnostic capability of the mGCL thickness is comparable to that of the pRNFL thickness in patients with early glaucoma. The inferior-outer sector of IML has a better diagnostic capability than the inferior-inner sector of IML for detection of early glaucoma.
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Affiliation(s)
- Pei-Wen Lin
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Hsueh-Wen Chang
- Department of Biological Sciences, Sun Yat-Sen University, Kaohsiung, Taiwan
| | - Jih-Pin Lin
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
| | - Ing-Chou Lai
- Department of Ophthalmology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, Taiwan
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