Can Probability Maps of Swept-Source Optical Coherence Tomography Predict Visual Field Changes in Preperimetric Glaucoma?

Lee, Won June; Kim, Young Kook; Jeoung, Jin Wook; Park, Ki Ho

doi:10.1167/iovs.17-22697

Cited by 10 publications

(7 citation statements)

References 39 publications

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“…Hood et al 31 reported that a single wide scan in SS-OCT contains the information needed to diagnose early glaucoma with excellent sensitivity and specificity. In another study, 32 structural changes shown on SS-OCT can detect or estimate VF damage even in preperimetric glaucoma.…”

Section: Discussionmentioning

confidence: 96%

Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between Devices

Shin

Kim

et al. 2021

Trans. Vis. Sci. Tech.

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To develop a deep learning model to estimate the visual field (VF) from spectral-domain optical coherence tomography (SD-OCT) and swept-source OCT (SS-OCT) and to compare the performance between them. Methods:Two deep learning models based on Inception-ResNet-v2 were trained to estimate 24-2 VF from SS-OCT and SD-OCT images. The estimation performance of the two models was evaluated by using the root mean square error between the actual and estimated VF. The performance was also compared among different glaucoma severities, Garway-Heath sectorizations, and central/peripheral regions. Results:The training dataset comprised images of 4391 eyes from 2350 subjects, and the test dataset was obtained from another 243 subjects (243 eyes). In all subjects, the global estimation errors were 5.29 ± 2.68 dB (SD-OCT) and 4.51 ± 2.54 dB (SS-OCT), and the estimation error of SS-OCT was significantly lower than that of SD-OCT (P < 0.001). In the analysis of sectors, SS-OCT showed better performance in all sectors except for the inferonasal sector in normal vision and early glaucoma. In advanced glaucoma, the estimation error of the central region was worsened in both OCTs, but SS-OCT was still significantly better in the peripheral region. Conclusions:Our deep learning model estimated the VF 24-2 better with a wide field image of SS-OCT than did with retinal nerve fiber layer and ganglion cell-inner plexiform layer images of SD-OCT.Translational Relevance: This deep learning method can help clinicians to determine the VF from OCT images. OCT manufacturers can equip this system to provide additional VF data.

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Section: Discussionmentioning

confidence: 96%

Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between Devices

Shin

Kim

et al. 2021

Trans. Vis. Sci. Tech.

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“…Shin et al 7 found that wide-field SS-OCT images were significantly more accurate than SD-OCT images at predicting a 24-2 VF. The authors suggested that wider scanning area of SS-OCT should contain much more information than the area of SD-OCT. Other studies have reported that the wide-field scan of SS-OCT collects the information needed to diagnose glaucoma with excellent sensitivity and specificity 14 , 22 , 23 . Therefore, a wide-field scan of SS-OCT images should better reflect the structural damage that corresponds to functional loss than the more narrow scan of SD-OCT.…”

Section: Discussionmentioning

confidence: 99%

Deep learning approaches to predict 10-2 visual field from wide-field swept-source optical coherence tomography en face images in glaucoma

Moon

Lee²,

Choi³

et al. 2022

Sci Rep

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Close monitoring of central visual field (VF) defects with 10-2 VF helps prevent blindness in glaucoma. We aimed to develop a deep learning model to predict 10-2 VF from wide-field swept-source optical coherence tomography (SS-OCT) images. Macular ganglion cell/inner plexiform layer thickness maps with either wide-field en face images (en face model) or retinal nerve fiber layer thickness maps (RNFLT model) were extracted, combined, and preprocessed. Inception-ResNet-V2 was trained to predict 10-2 VF from combined images. Estimation performance was evaluated using mean absolute error (MAE) between actual and predicted threshold values, and the two models were compared with different input data. The training dataset comprised paired 10-2 VF and SS-OCT images of 3,025 eyes of 1,612 participants and the test dataset of 337 eyes of 186 participants. Global prediction errors (MAEpoint-wise) were 3.10 and 3.17 dB for the en face and RNFLT models, respectively. The en face model performed better than the RNFLT model in superonasal and inferonasal sectors (P = 0.011 and P = 0.030). Prediction errors were smaller in the inferior versus superior hemifields for both models. The deep learning model effectively predicted 10-2 VF from wide-field SS-OCT images and might help clinicians efficiently individualize the frequency of 10-2 VF in clinical practice.

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“…18,20,21,[26][27][28] However, their OCT device was a prototype and not commercially available, and their software also was under development; moreover, they did not evaluate pro- gression with serial OCT maps. After wide-field SS-OCT scanning and an SS-OCT probability map became commercially available, the diagnostic performance of wide-field SS-OCT RNFL maps for early glaucoma 29 and of the study by Hood 20 for prediction of future VF changes in preperimetric glaucoma patients, 30 respectively, were evaluated. However, structural progression was not evaluated in either study.…”

Section: Discussionmentioning

confidence: 99%

Serial Combined Wide-Field Optical Coherence Tomography Maps for Detection of Early Glaucomatous Structural Progression

Lee

Kim

et al. 2018

JAMA Ophthalmol

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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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Can Probability Maps of Swept-Source Optical Coherence Tomography Predict Visual Field Changes in Preperimetric Glaucoma?

Abstract: Structural changes on SS-OCT probability maps could detect or predict VF changes using SAP, in a considerable number of PPG eyes. Careful comparison of probability maps with SAP results could be useful in diagnosing and monitoring PPG patients in the clinical setting.

Cited by 10 publications

References 39 publications

Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between Devices

Visual Field Inference From Optical Coherence Tomography Using Deep Learning Algorithms: A Comparison Between Devices

Deep learning approaches to predict 10-2 visual field from wide-field swept-source optical coherence tomography en face images in glaucoma

Serial Combined Wide-Field Optical Coherence Tomography Maps for Detection of Early Glaucomatous Structural Progression

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