Prediction of visual field from swept-source optical coherence tomography using deep learning algorithms

Park, Keunheung; Kim, Jinmi; Kim, Sangyoon; Shin, Jonghoon

doi:10.1007/s00417-020-04909-z

Cited by 13 publications

(14 citation statements)

References 29 publications

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“…Many models have been reported to discriminate between glaucoma eyes and nonglaucoma eyes using OCT. 6 , 7 , 24 , 25 Limited models have also been developed to predict VF measurements from OCT, but predicted measurements were usually mean VF sensitivity or sectoral VF sensitivity. 10 , 13 In this research we predicted TH values in a pointwise manner; as shown in a recent paper, accurate pointwise predictions are more difficult than sectorial prediction. 11 The importance of pointwise prediction cannot be overstated when considering the application of such a model to real-world clinical settings.…”

Section: Discussionmentioning

confidence: 82%

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Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field

Hashimoto

Kiwaki

Sugiura

et al. 2021

Trans. Vis. Sci. Tech.

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Purpose To investigate whether a correction based on a Humphrey field analyzer (HFA) 24-2/30-2 visual field (VF) can improve the prediction performance of a deep learning model to predict the HFA 10-2 VF test from macular optical coherence tomography (OCT) measurements. Methods This is a multicenter, cross-sectional study. The training dataset comprised 493 eyes of 285 subjects (407, open-angle glaucoma [OAG]; 86, normative) who underwent HFA 10-2 testing and macular OCT. The independent testing dataset comprised 104 OAG eyes of 82 subjects who had undergone HFA 10-2 test, HFA 24-2/30-2 test, and macular OCT. A convolutional neural network (CNN) DL model was trained to predict threshold sensitivity (TH) values in HFA 10-2 from retinal thickness measured by macular OCT. The predicted TH values was modified by pattern-based regularization (PBR) and corrected with HFA 24-2/30-2. Absolute error (AE) of mean TH values and mean absolute error (MAE) of TH values were compared between the CNN-PBR alone model and the CNN-PBR corrected with HFA 24-2/30-2. Results AE of mean TH values was lower in the CNN-PBR with HFA 24-2/30-2 correction than in the CNN-PBR alone (1.9dB vs. 2.6dB; P = 0.006). MAE of TH values was lower in the CNN-PBR with correction compared to the CNN-PBR alone (4.2dB vs. 5.3 dB; P < 0.001). The inferior temporal quadrant showed lower prediction errors compared with other quadrants. Conclusions The performance of a DL model to predict 10-2 VF from macular OCT was improved by the correction with HFA 24-2/30-2. Translational Relevance This model can reduce the burden of additional HFA 10-2 by making the best use of routinely performed HFA 24-2/30-2 and macular OCT.

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Section: Discussionmentioning

confidence: 82%

“…8 , 9 Recent research has also demonstrated the potential for DL models to predict VF sensitivity from OCT images in patients with glaucoma. 10 – 14…”

Section: Introductionmentioning

confidence: 99%

Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field

Hashimoto

Kiwaki

Sugiura

et al. 2021

Trans. Vis. Sci. Tech.

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“…Fundus imaging, especially optical coherence tomography (OCT), is recognized as a key factor for visual prediction (Guo et al, 2017;Esaka et al, 2019;Park et al, 2020). OCT images have been applied to predict prognostic visual function in agerelated macular degeneration (AMD) (Rohm et al, 2018) and have achieved excellent performance in visual prediction.…”

Section: Introductionmentioning

confidence: 99%

Longtime Vision Function Prediction in Childhood Cataract Patients Based on Optical Coherence Tomography Images

Xiang

Chen

et al. 2021

Front. Bioeng. Biotechnol.

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The results of visual prediction reflect the tendency and speed of visual development during a future period, based on which ophthalmologists and guardians can know the potential visual prognosis in advance, decide on an intervention plan, and contribute to visual development. In our study, we developed an intelligent system based on the features of optical coherence tomography images for long-term prediction of best corrected visual acuity (BCVA) 3 and 5 years in advance. Two hundred eyes of 132 patients were included. Six machine learning algorithms were applied. In the BCVA predictions, small errors within two lines of the visual chart were achieved. The mean absolute errors (MAEs) between the prediction results and ground truth were 0.1482–0.2117 logMAR for 3-year predictions and 0.1198–0.1845 logMAR for 5-year predictions; the root mean square errors (RMSEs) were 0.1916–0.2942 logMAR for 3-year predictions and 0.1692–0.2537 logMAR for 5-year predictions. This is the first study to predict post-therapeutic BCVAs in young children. This work establishes a reliable method to predict prognosis 5 years in advance. The application of our research contributes to the design of visual intervention plans and visual prognosis.

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“…Or there might be a difference in the feasibility of the architecture for OCT B-scans and chest radiographs, as OCT B-scans have lower complexity and pixel signals compared with chest radiograph images. More evidence from other studies supports better performance of deep rather than shallow CNNs for automated classification of OCT images [26][27][28]. These deeper CNNs also demonstrated good performance on the independent test set with an accuracy of 78.00-92.00%.…”

Section: Discussionmentioning

confidence: 63%

Classification of pachychoroid on optical coherence tomography using deep learning

Kang

Lee

et al. 2021

Graefes Arch Clin Exp Ophthalmol

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Purpose Pachychoroid is characterized by dilated Haller vessels and choriocapillaris attenuation that are seen on optical coherence tomography (OCT) B-scans. This study investigated the feasibility of using deep learning (DL) models to classify pachychoroid and non-pachychoroid eyes from OCT B-scan images. Methods In total, 1898 OCT B-scan images were collected from eyes with macular diseases. Images were labeled as pachychoroid or non-pachychoroid based on strict quantitative and qualitative criteria for multimodal imaging analysis by two retina specialists. DL models were trained (80%) and validated (20%) using pretrained convolutional neural networks (CNNs). Model performance was assessed using an independent test set of 50 non-pachychoroid and 50 pachychoroid images. Results The final accuracy of AlexNet and VGG-16 was 57.52% for both models. ResNet50, Inception-v3, Inception-ResNet-v2, and Xception showed a final accuracy of 96.31%, 95.25%, 93.40%, and 92.61%, respectively, for the validation set. These models demonstrated accuracy on an independent test set of 78.00%, 86.00%, 90.00%, and 92.00%, and an F1 score of 0.718, 0.841, 0.894, and 0.920, respectively. Conclusion DL models classified pachychoroid and non-pachychoroid images with good performance. Accurate classification can be achieved using CNN models with deep rather than shallow neural networks.

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Prediction of visual field from swept-source optical coherence tomography using deep learning algorithms

Cited by 13 publications

References 29 publications

Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field

Predicting 10-2 Visual Field From Optical Coherence Tomography in Glaucoma Using Deep Learning Corrected With 24-2/30-2 Visual Field

Longtime Vision Function Prediction in Childhood Cataract Patients Based on Optical Coherence Tomography Images

Classification of pachychoroid on optical coherence tomography using deep learning

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