Optical coherence tomography-based deep-learning model for detecting central serous chorioretinopathy

Abstract Central serous chorioretinopathy (CSC) is a common condition characterized by serous detachment of the neurosensory retina at the posterior pole. We built a deep learning system model to diagnose CSC, and distinguish chronic from acute CSC using spectral domain optical coherence tomography (SD-OCT) images. Data from SD-OCT images of patients with CSC and a control group were analyzed with a convolutional neural network. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were used to evaluate the model. For CSC diagnosis, our model showed an accuracy, sensitivity, and specificity of 93.8%, 90.0%, and 99.1%, respectively; AUROC was 98.9% (95% CI, 0.983–0.995); and its diagnostic performance was comparable with VGG-16, Resnet-50, and the diagnoses of five different ophthalmologists. For distinguishing chronic from acute cases, the accuracy, sensitivity, and specificity were 97.6%, 100.0%, and 92.6%, respectively; AUROC was 99.4% (95% CI, 0.985–1.000); performance was better than VGG-16 and Resnet-50, and was as good as the ophthalmologists. Our model performed well when diagnosing CSC and yielded highly accurate results when distinguishing between acute and chronic cases. Thus, automated deep learning system algorithms could play a role independent of human experts in the diagnosis of CSC.

Download Full-text

HyCAD-OCT: A Hybrid Computer-Aided Diagnosis of Retinopathy by Optical Coherence Tomography Integrating Machine Learning and Feature Maps Localization

Applied Sciences ◽

10.3390/app10144716 ◽

2020 ◽

Vol 10 (14) ◽

pp. 4716 ◽

Cited By ~ 1

Author(s):

Mohamed Ramzy Ibrahim ◽

Karma M. Fathalla ◽

Sherin M. Youssef

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Sensitivity And Specificity ◽

Learning System ◽

Substantial Contribution ◽

Optical Coherence ◽

Feature Maps ◽

Wide Range ◽

Computer Aided ◽

Hybrid Computer

Optical Coherence Tomography (OCT) imaging has major advantages in effectively identifying the presence of various ocular pathologies and detecting a wide range of macular diseases. OCT examinations can aid in the detection of many retina disorders in early stages that could not be detected in traditional retina images. In this paper, a new hybrid computer-aided OCT diagnostic system (HyCAD) is proposed for classification of Diabetic Macular Edema (DME), Choroidal Neovascularization (CNV) and drusen disorders, while separating them from Normal OCT images. The proposed HyCAD hybrid learning system integrates the segmentation of Region of Interest (RoI), based on central serious chorioretinopathy (CSC) in Spectral Domain Optical Coherence Tomography (SD-OCT) images, with deep learning architectures for effective diagnosis of retinal disorders. The proposed system assimilates a range of techniques including RoI localization and feature extraction, followed by classification and diagnosis. An efficient feature fusion phase has been introduced for combining the OCT image features, extracted by Deep Convolutional Neural Network (CNN), with the features extracted from the RoI segmentation phase. This fused feature set is used to predict multiclass OCT retina disorders. The proposed segmentation phase of retinal RoI regions adds substantial contribution as it draws attention to the most significant areas that are candidate for diagnosis. A new modified deep learning architecture (Norm-VGG16) is introduced integrating a kernel regularizer. Norm-VGG16 is trained from scratch on a large benchmark dataset and used in RoI localization and segmentation. Various experiments have been carried out to illustrate the performance of the proposed system. Large Dataset of Labeled Optical Coherence Tomography (OCT) v3 benchmark is used to validate the efficiency of the model compared with others in literature. The experimental results show that the proposed model achieves relatively high-performance in terms of accuracy, sensitivity and specificity. An average accuracy, sensitivity and specificity of 98.8%, 99.4% and 98.2% is achieved, respectively. The remarkable performance achieved reflects that the fusion phase can effectively improve the identification ratio of the urgent patients’ diagnostic images and clinical data. In addition, an outstanding performance is achieved compared to others in literature.

Download Full-text

Classifying central serous chorioretinopathy subtypes with a deep neural network using optical coherence tomography images: a cross-sectional study

Scientific Reports ◽

10.1038/s41598-021-04424-z ◽

2022 ◽

Vol 12 (1) ◽

Author(s):

Jeewoo Yoon ◽

Jinyoung Han ◽

Junseo Ko ◽

Seong Choi ◽

Ji In Park ◽

...

Keyword(s):

Neural Network ◽

Optical Coherence Tomography ◽

Deep Learning ◽

Central Serous Chorioretinopathy ◽

Cross Validation ◽

Learning Systems ◽

Test Accuracy ◽

Optical Coherence ◽

Proposed Model ◽

Sd Oct

AbstractCentral serous chorioretinopathy (CSC) is the fourth most common retinopathy and can reduce quality of life. CSC is assessed using optical coherence tomography (OCT), but deep learning systems have not been used to classify CSC subtypes. This study aimed to build a deep learning system model to distinguish CSC subtypes using a convolutional neural network (CNN). We enrolled 435 patients with CSC from a single tertiary center between January 2015 and January 2020. Data from spectral domain OCT (SD-OCT) images of the patients were analyzed using a deep CNN. Five-fold cross-validation was employed to evaluate the model’s ability to discriminate acute, non-resolving, inactive, and chronic atrophic CSC. We compared the performances of the proposed model, Resnet-50, Inception-V3, and eight ophthalmologists. Overall, 3209 SD-OCT images were included. The proposed model showed an average cross-validation accuracy of 70.0% (95% confidence interval [CI], 0.676–0.718) and the highest test accuracy was 73.5%. Additional evaluation in an independent set of 104 patients demonstrated the reliable performance of the proposed model (accuracy: 76.8%). Our model could classify CSC subtypes with high accuracy. Thus, automated deep learning systems could be useful in the classification and management of CSC.

Download Full-text

Development and validation of a deep learning system to screen vision-threatening conditions in high myopia using optical coherence tomography images

British Journal of Ophthalmology ◽

10.1136/bjophthalmol-2020-317825 ◽

2020 ◽

pp. bjophthalmol-2020-317825

Author(s):

Yonghao Li ◽

Weibo Feng ◽

Xiujuan Zhao ◽

Bingqian Liu ◽

Yan Zhang ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

High Myopia ◽

Large Scale ◽

Learning System ◽

Youden Index ◽

Optical Coherence ◽

Test Dataset ◽

Independent Test ◽

Independent Test Dataset

Background/aimsTo apply deep learning technology to develop an artificial intelligence (AI) system that can identify vision-threatening conditions in high myopia patients based on optical coherence tomography (OCT) macular images.MethodsIn this cross-sectional, prospective study, a total of 5505 qualified OCT macular images obtained from 1048 high myopia patients admitted to Zhongshan Ophthalmic Centre (ZOC) from 2012 to 2017 were selected for the development of the AI system. The independent test dataset included 412 images obtained from 91 high myopia patients recruited at ZOC from January 2019 to May 2019. We adopted the InceptionResnetV2 architecture to train four independent convolutional neural network (CNN) models to identify the following four vision-threatening conditions in high myopia: retinoschisis, macular hole, retinal detachment and pathological myopic choroidal neovascularisation. Focal Loss was used to address class imbalance, and optimal operating thresholds were determined according to the Youden Index.ResultsIn the independent test dataset, the areas under the receiver operating characteristic curves were high for all conditions (0.961 to 0.999). Our AI system achieved sensitivities equal to or even better than those of retina specialists as well as high specificities (greater than 90%). Moreover, our AI system provided a transparent and interpretable diagnosis with heatmaps.ConclusionsWe used OCT macular images for the development of CNN models to identify vision-threatening conditions in high myopia patients. Our models achieved reliable sensitivities and high specificities, comparable to those of retina specialists and may be applied for large-scale high myopia screening and patient follow-up.

Download Full-text

OCTAI: Smartphone-based Optical Coherence Tomography Image Analysis System

10.31224/osf.io/gq89c ◽

2021 ◽

Author(s):

Adrit Rao ◽

Harvey A. Fishman

Keyword(s):

Optical Coherence Tomography ◽

Image Analysis ◽

Deep Learning ◽

Early Stage ◽

Real Life ◽

Learning System ◽

Optical Coherence Tomography Image ◽

Image Analysis System ◽

Optical Coherence ◽

Oct Image

Identifying diseases in Optical Coherence Tomography (OCT) images using Deep Learning models and methods is emerging as a powerful technique to enhance clinical diagnosis. Identifying macular diseases in the eye at an early stage and preventing misdiagnosis is crucial. The current methods developed for OCT image analysis have not yet been integrated into an accessible form-factor that can be utilized in a real-life scenario by Ophthalmologists. Additionally, current methods do not employ robust multiple metric feedback. This paper proposes a highly accurate smartphone-based Deep Learning system, OCTAI, that allows a user to take an OCT picture and receive real-time feedback through on-device inference. OCTAI analyzes the input OCT image in three different ways: (1) full image analysis, (2) quadrant based analysis, and (3) disease detection based analysis. With these three analysis methods, along with an Ophthalmologist's interpretation, a robust diagnosis can potentially be made. The ultimate goal of OCTAI is to assist Ophthalmologists in making a diagnosis through a digital second opinion and enabling them to cross-check their diagnosis before making a decision based on purely manual analysis of OCT images. OCTAI has the potential to allow Ophthalmologists to improve their diagnosis and may reduce misdiagnosis rates, leading to faster treatment of diseases.

Download Full-text

Discriminating glaucomatous and compressive optic neuropathy on spectral-domain optical coherence tomography with deep learning classifier

British Journal of Ophthalmology ◽

10.1136/bjophthalmol-2019-314330 ◽

2020 ◽

Vol 104 (12) ◽

pp. 1717-1723 ◽

Cited By ~ 1

Author(s):

Jinho Lee ◽

Jin-Soo Kim ◽

Haeng Jin Lee ◽

Seong-Joon Kim ◽

Young Kook Kim ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Optic Neuropathy ◽

Spectral Domain ◽

Optical Coherence ◽

Compressive Optic Neuropathy ◽

Diagnostic Parameters ◽

Learning Classifier ◽

Oct Image ◽

Sd Oct

Background/aimsTo assess the performance of a deep learning classifier for differentiation of glaucomatous optic neuropathy (GON) from compressive optic neuropathy (CON) based on ganglion cell–inner plexiform layer (GCIPL) and retinal nerve fibre layer (RNFL) spectral-domain optical coherence tomography (SD-OCT).MethodsEighty SD-OCT image sets from 80 eyes of 80 patients with GON along with 81 SD-OCT image sets from 54 eyes of 54 patients with CON were compiled for the study. The bottleneck features extracted from the GCIPL thickness map, GCIPL deviation map, RNFL thickness map and RNFL deviation map were used as predictors for the deep learning classifier. The area under the receiver operating characteristic curve (AUC) was calculated to validate the diagnostic performance. The AUC with the deep learning classifier was compared with those for conventional diagnostic parameters including temporal raphe sign, SD-OCT thickness profile and standard automated perimetry.ResultsThe deep learning system achieved an AUC of 0.990 (95% CI 0.982 to 0.999) with a sensitivity of 97.9% and a specificity of 92.6% in a fivefold cross-validation testing, which was significantly larger than the AUCs with the other parameters: 0.804 (95% CI 0.737 to 0.872) with temporal raphe sign, 0.815 (95% CI 0.734 to 0.896) with superonasal GCIPL and 0.776 (95% CI 0.691 to 0.860) with superior GCIPL thicknesses (all p<0.001).ConclusionThe deep learning classifier can outperform the conventional diagnostic parameters for discrimination of GON and CON on SD-OCT.

Download Full-text

Deep Learning Based Sub-Retinal Fluid Segmentation in Central Serous Chorioretinopathy Optical Coherence Tomography Scans

2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2019.8857105 ◽

2019 ◽

Author(s):

T J Narendra Rao ◽

G N Girish ◽

Abhishek R. Kothari ◽

Jeny Rajan

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Central Serous Chorioretinopathy ◽

Optical Coherence

Download Full-text

A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis

10.2337/figshare.14710284.v1 ◽

2021 ◽

Author(s):

Fangyao Tang ◽

Xi Wang ◽

An-ran Ran ◽

Carmen KM Chan ◽

Mary Ho ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Macular Edema ◽

Diabetic Macular Edema ◽

Vision Loss ◽

Three Dimensional ◽

Learning System ◽

Optical Coherence ◽

Automated Classification

<a>Objective:</a> Diabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep-learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices. Research Design and Methods: We trained and validated two versions of a multi-task convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume-scans and two-dimensional (2D) B-scans respectively. For both 3D and 2D CNNs, we employed the residual network (ResNet) as the backbone. For the 3D CNN, we used a 3D version of ResNet-34 with the last fully connected layer removed as the feature extraction module. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent datasets from Singapore, Hong Kong, the US, China, and Australia. Results: In classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920–0.954), 0.958 (0.930–0.977), and 0.965 (0.948–0.977) for primary dataset obtained from Cirrus, Spectralis, and Triton OCTs respectively, in addition to AUROCs greater than 0.906 for the external datasets. For the further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940–0.995), 0.951 (0.898–0.982), and 0.975 (0.947–0.991) for the primary dataset and greater than 0.894 for the external datasets. Conclusion: We demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which may potentially create a more effective triaging mechanism to eye clinics.

Download Full-text

A multi-task deep-learning system for assessment of diabetic macular ischemia on optical coherence tomography angiography images

Retina ◽

10.1097/iae.0000000000003287 ◽

2021 ◽

Vol Publish Ahead of Print ◽

Author(s):

Dawei Yang ◽

Zihan Sun ◽

Jian Shi ◽

Anran Ran ◽

Fangyao Tang ◽

...

Keyword(s):

Optical Coherence Tomography ◽

Deep Learning ◽

Optical Coherence Tomography Angiography ◽

Learning System ◽

Optical Coherence ◽

Macular Ischemia

Download Full-text

A pilot study of scleral thickness in central serous chorioretinopathy using anterior segment optical coherence tomography

Scientific Reports ◽

10.1038/s41598-021-85229-y ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Yun Ji Lee ◽

Yeon Jeong Lee ◽

Jae Yeon Lee ◽

Suhwan Lee

Keyword(s):

Optical Coherence Tomography ◽

Central Serous Chorioretinopathy ◽

Anterior Segment ◽

Venous Drainage ◽

Enhanced Depth Imaging ◽

Control Group ◽

Study Group ◽

Optical Coherence ◽

Swept Source ◽

Significant Difference

AbstractThe aim of this study is to compare the scleral thickness of central serous chorioretinopathy (CSC) eyes with controls using anterior segment optical coherence tomography (AS OCT). This prospective case control study included 15 patients (15 eyes) with CSC and 15 age and gender matched healthy subjects. All subjects underwent spectral domain OCT with enhanced depth imaging and swept source AS OCT of temporal sclera. We investigated difference in scleral thickness between the two groups and relationship between choroidal and scleral thickness. Among the 15 eyes in the study group, 1 eye had acute CSC, 4 had recurrent CSC, 7 had inactive CSC, and 3 had chronic CSC. There was no significant difference in terms of age, gender, axial length and spherical equivalent between the two groups. The choroidal and scleral thickness of the study group were significantly greater than those of the control group (P < 0.001, P = 0.034). Choroidal thickness was positively correlated with scleral thickness (P = 0.031). A thick sclera along with a thick choroid were demonstrated in CSC eyes using AS OCT. Scleral characteristics might be involved in the pathogenesis of CSC by affecting outflow resistance of venous drainage in choroidal circulation.

Download Full-text