Prediction of neovascular age-related macular degeneration recurrence using optical coherence tomography images with a deep neural network.

Neovascular age-related macular degeneration (nAMD) can result in blindness if left untreated, and patients often require repeated anti-vascular endothelial growth factor injections. Although, the treat-and-extend method is becoming popular to reduce vision loss attributed to recurrence, it may pose a risk of overtreatment. This study aimed to develop a deep learning model based on DenseNet201 to predict nAMD recurrence within 3 months after confirming dry-up 1 month following three loading injections in treatment-naïve patients. A dataset of 1076 spectral domain optical coherence tomography (OCT) images from 269 patients diagnosed with nAMD was used. The performance of the model was compared with that of 6 ophthalmologists, using 100 randomly selected samples. The DenseNet201-based model achieved 53.0% accuracy in predicting nAMD recurrence using a single pre-injection image and 60.2% accuracy after viewing all the images immediately after the 1st, 2nd, and 3rd injections. The model outperformed experienced ophthalmologists, with an average accuracy of 52.17% using a single pre-injection image and 53.3% after examining four images before and after three loading injections. In conclusion, the artificial intelligence model demonstrated a promising ability to predict nAMD recurrence using OCT images and outperformed experienced ophthalmologists. These findings suggest that deep learning models can assist in nAMD recurrence prediction, thus improving patient outcomes and optimizing treatment strategies.

Developing and Evaluating an AI-Based Computer-Aided Diagnosis System for Retinal Disease: Diagnostic Study for Central Serous Chorioretinopathy.

BACKGROUND: Although previous research has made substantial progress in developing high-performance artificial intelligence (AI)-based computer-aided diagnosis (AI-CAD) systems in various medical domains, little attention has been paid to developing and evaluating AI-CAD system in ophthalmology, particularly for diagnosing retinal diseases using optical coherence tomography (OCT) images. OBJECTIVE: This diagnostic study aimed to determine the usefulness of a proposed AI-CAD system in assisting ophthalmologists with the diagnosis of central serous chorioretinopathy (CSC), which is known to be difficult to diagnose, using OCT images. METHODS: For the training and evaluation of the proposed deep learning model, 1693 OCT images were collected and annotated. The data set included 929 and 764 cases of acute and chronic CSC, respectively. In total, 66 ophthalmologists (2 groups: 36 retina and 30 nonretina specialists) participated in the observer performance test. To evaluate the deep learning algorithm used in the proposed AI-CAD system, the training, validation, and test sets were split in an 8:1:1 ratio. Further, 100 randomly sampled OCT images from the test set were used for the observer performance test, and the participants were instructed to select a CSC subtype for each of these images. Each image was provided under different conditions: (1) without AI assistance, (2) with AI assistance with a probability score, and (3) with AI assistance with a probability score and visual evidence heatmap. The sensitivity, specificity, and area under the receiver operating characteristic curve were used to measure the diagnostic performance of the model and ophthalmologists. RESULTS: The proposed system achieved a high detection performance (99% of the area under the curve) for CSC, outperforming the 66 ophthalmologists who participated in the observer performance test. In both groups, ophthalmologists with the support of AI assistance with a probability score and visual evidence heatmap achieved the highest mean diagnostic performance compared with that of those subjected to other conditions (without AI assistance or with AI assistance with a probability score). Nonretina specialists achieved expert-level diagnostic performance with the support of the proposed AI-CAD system. CONCLUSIONS: Our proposed AI-CAD system improved the diagnosis of CSC by ophthalmologists, which may support decision-making regarding retinal disease detection and alleviate the workload of ophthalmologists.

Prediction of Visual Acuity in Pathologic Myopia with Myopic Choroidal Neovascularization Treated with Anti-Vascular Endothelial Growth Factor Using a Deep Neural Network Based on Optical Coherence Tomography Images.

Myopic choroidal neovascularization (mCNV) is a common cause of vision loss in patients with pathological myopia. However, predicting the visual prognosis of patients with mCNV remains challenging. This study aimed to develop an artificial intelligence (AI) model to predict visual acuity (VA) in patients with mCNV. This study included 279 patients with mCNV at baseline; patient data were collected, including optical coherence tomography (OCT) images, VA, and demographic information. Two models were developed: one comprising horizontal/vertical OCT images (H/V cuts) and the second comprising 25 volume scan images. The coefficient of determination (R2) and root mean square error (RMSE) were computed to evaluate the performance of the trained network. The models achieved high performance in predicting VA after 1 (R2 = 0.911, RMSE = 0.151), 2 (R2 = 0.894, RMSE = 0.254), and 3 (R2 = 0.891, RMSE = 0.227) years. Using multiple-volume scanning, OCT images enhanced the performance of the models relative to using only H/V cuts. This study proposes AI models to predict VA in patients with mCNV. The models achieved high performance by incorporating the baseline VA, OCT images, and post-injection data. This model could assist in predicting the visual prognosis and evaluating treatment outcomes in patients with mCNV undergoing intravitreal anti-vascular endothelial growth factor therapy.

Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolutional Network.

Neovascular age-related macular degeneration (nAMD) and central serous chorioretinopathy (CSC) are two of the most common macular diseases. This study proposes a convolutional neural network (CNN)-based deep learning model for classifying the subtypes of nAMD (polypoidal choroidal vasculopathy, retinal angiomatous proliferation, and typical nAMD) and CSC (chronic CSC and acute CSC) and healthy individuals using single spectral-domain optical coherence tomography (SD-OCT) images. The proposed model was trained and tested using 6063 SD-OCT images from 521 patients and 47 healthy participants. We used three well-known CNN architectures (VGG-16, VGG-19, and ResNet) and two customized classification layers. Additionally, transfer learning and mix-up-based data augmentation were applied to improve robustness and accuracy. Our model demonstrated high accuracies of 99.7% and 91.1% in the nAMD and CSC classification and retinopathy (nAMD and CSC) subtype classification, including normal participants, respectively. Furthermore, we performed an external test to compare the classification accuracy with that of eight ophthalmologists, and our model showed the highest accuracy. The region determined to be important for classification by the model was confirmed using gradient-weighted class activation mapping. The model's clinical criteria were similar to that of the ophthalmologists.

Classifying neovascular age-related macular degeneration with a deep convolutional neural network based on optical coherence tomography images.

Neovascular age-related macular degeneration (nAMD) is among the main causes of visual impairment worldwide. We built a deep learning model to distinguish the subtypes of nAMD using spectral domain optical coherence tomography (SD-OCT) images. Data from SD-OCT images of nAMD (polypoidal choroidal vasculopathy, retinal angiomatous proliferation, and typical nAMD) and normal healthy patients were analyzed using a convolutional neural network (CNN). The model was trained and validated based on 4749 SD-OCT images from 347 patients and 50 healthy controls. To adopt an accurate and robust image classification architecture, we evaluated three well-known CNN structures (VGG-16, VGG-19, and ResNet) and two customized classification layers (fully connected layer with dropout vs. global average pooling). Following the test set performance, the model with the highest classification accuracy was used. Transfer learning and data augmentation were applied to improve the robustness and accuracy of the model. Our proposed model showed an accuracy of 87.4% on the test data (920 images), scoring higher than ten ophthalmologists, for the same data. Additionally, the part that our model judged to be important in classification was confirmed through Grad-CAM images, and consequently, it has a similar judgment criteria to that of ophthalmologists. Thus, we believe that our model can be used as an auxiliary tool in clinical practice.

Assessing central serous chorioretinopathy with deep learning and multiple optical coherence tomography images.

Central serous chorioretinopathy (CSC) is one of the most common macular diseases that can reduce the quality of life of patients. This study aimed to build a deep learning-based classification model using multiple spectral domain optical coherence tomography (SD-OCT) images together to diagnose CSC. Our proposed system contains two modules: single-image prediction (SIP) and a final decision (FD) classifier. A total of 7425 SD-OCT images from 297 participants (109 acute CSC, 106 chronic CSC, 82 normal) were included. In the fivefold cross validation test, our model showed an average accuracy of 94.2%. Compared to other end-to-end models, for example, a 3D convolutional neural network (CNN) model and a CNN-long short-term memory (CNN-LSTM) model, the proposed system showed more than 10% higher accuracy. In the experiments comparing the proposed model and ophthalmologists, our model showed higher accuracy than experts in distinguishing between acute, chronic, and normal cases. Our results show that an automated deep learning-based model could play a supplementary role alongside ophthalmologists in the diagnosis and management of CSC. In particular, the proposed model seems clinically applicable because it can classify CSCs using multiple OCT images simultaneously.

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

Central 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.

Distinguishing retinal angiomatous proliferation from polypoidal choroidal vasculopathy with a deep neural network based on optical coherence tomography.

This cross-sectional study aimed to build a deep learning model for detecting neovascular age-related macular degeneration (AMD) and to distinguish retinal angiomatous proliferation (RAP) from polypoidal choroidal vasculopathy (PCV) using a convolutional neural network (CNN). Patients from a single tertiary center were enrolled from January 2014 to January 2020. Spectral-domain optical coherence tomography (SD-OCT) images of patients with RAP or PCV and a control group were analyzed with a deep CNN. Sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC) were used to evaluate the model's ability to distinguish RAP from PCV. The performances of the new model, the VGG-16, Resnet-50, Inception, and eight ophthalmologists were compared. A total of 3951 SD-OCT images from 314 participants (229 AMD, 85 normal controls) were analyzed. In distinguishing the PCV and RAP cases, the proposed model showed an accuracy, sensitivity, and specificity of 89.1%, 89.4%, and 88.8%, respectively, with an AUROC of 95.3% (95% CI 0.727-0.852). The proposed model showed better diagnostic performance than VGG-16, Resnet-50, and Inception-V3 and comparable performance with the eight ophthalmologists. The novel model performed well when distinguishing between PCV and RAP. Thus, automated deep learning systems may support ophthalmologists in distinguishing RAP from PCV.

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

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.

Comparative analysis of macular microstructure by spectral-domain optical coherence tomography before and after silicone oil removal.

PURPOSE: To document comparative analysis of macular microstructures before and after silicone oil (SO) removal via spectral-domain optical coherence tomography and to assess the retinal changes associated with visual outcome. METHODS: Forty-six eyes that underwent vitrectomy with SO tamponade were included. Ophthalmic examinations were performed before SO removal and at Months 1, 3, and 6 postoperatively including best-corrected visual acuity and spectral-domain optical coherence tomography. The macular microstructures identified by spectral-domain optical coherence tomography were compared before and after SO removal, and tomographic parameters related to visual outcome were analyzed. RESULTS: Under SO tamponade, spectral-domain optical coherence tomography demonstrated macular tomographic findings: epiretinal membrane in 12 eyes (26.1%), cystoid macular edema in 9 (19.6%), undulated inner retina in 8 (17.4%), and submacular fluid in 4 (8.7%). The mean duration of SO tamponade was significantly longer in eyes with macular changes (6.3 ± 4.6 months) than those without changes (5.2 ± 4.4 months) (P = 0.02). A total of 13 eyes had peeling of epiretinal membrane or internal limiting membrane combined with SO removal. After SO removal, most of microstructural changes were resolved. In the eyes with macular epiretinal membrane or cystoid macular edema, final best-corrected visual acuity was significantly improved compared with baseline (P = 0.017, 0.049), which paralleled the decrease of central foveal thickness. Restoration of photoreceptor layer and external limiting membrane was achieved in 2 (4.9%) and 5 eyes (12.5%), and those with continuous photoreceptor layer or external limiting membrane had the better final best-corrected visual acuity. CONCLUSION: Under SO tamponade, macular microstructural changes were identified by spectral-domain optical coherence tomography and were associated with duration of SO tamponade. Most of the microstructural changes were recovered after SO removal, if needed, combined with macular surgery. Anatomic resolution was accompanied by postoperative visual improvement.

Effects of multicurve RGP contact lens use on topographic changes in keratoconus.

PURPOSE: To evaluate the effects of wearing rigid gas permeable (RGP) contact lenses on the topographic changes in keratoconus. METHODS: Seventy-seven keratoconic eyes that wore multicurve RGP contact lenses and 30 keratoconic eyes that wore no contact lenses were retrospectively analyzed. The mean follow-ups were 22.6 and 20.5 months in the lens-wearing and control groups, respectively. Visual acuity, comfort, daily wearing time, and corneal staining were evaluated for both groups. The changes in topographic indices were compared between the lens-wearing and control groups. RESULTS: Multicurve RGP lens corrected logarithm of the minimum angle of resolution visual acuity of the lens-wearing group significantly improved from -0.016+/-0.065 to -0.032+/-0.10 at follow-up (p=0.05). In the lens-wearing group with advanced keratoconus, the Sim Kmax, Sim Kmin, apical power, astigmatic index, and anterior elevation significantly decreased from 57.68+/-4.26 diopter (D), 50.50+/-2.32 D, 62.79+/-5.11 D, 7.20+/-0.55 D and 67.36+/-16.30 microm to 55.51+/-4.28 D, 49.62+/-3.26 D, 60.31+/-5.41 D, 5.90+/-0.51 D and 60.61+/-16.09 microm, respectively (paired t-test, p<0.05). The irregularity index of 3 mm did not significantly change. Meanwhile, in the control group, the apical power and irregularity index increased from 55.56+/-7.25 D and 3.06+/-1.68 D to 57.11+/-7.75 D and 3.25+/-1.71 D, respectively (paired t-test, p=0.008, p=0.01). CONCLUSIONS: Properly fitted multicurve RGP contact lenses are not likely to contribute to the progression of keratoconus.

Clinical characteristics of multiple sclerosis and associated optic neuritis in Korean children.

PURPOSE: To study the clinical characteristics of multiple sclerosis and associated optic neuritis in Korean children. METHOD: A retrospective analysis was performed on 10 patients with an onset of multiple sclerosis before age 16. Information on sex, age of onset, clinical course, laboratory findings, and clinical characteristics of optic neuritis was obtained. RESULT: The mean age at presentation was 7.31 +/- 2.99 years, and the mean duration of observation was 36.2 +/- 26.1 months. No female predilection (50%) was observed. The disease presented as relapsing-remitting type multiple sclerosis in all patients and transited to secondary progressive type in two cases (20%). No oligoclonal bands were found in any patient. Optic neuritis occurred in eight patients (80%); five (62.5%) of these had optic neuritis at the first multiple sclerosis attack, with all five manifesting bilateral simultaneous optic neuritis. Visual acuity recovered to > or =20/40 in 8 of 15 eyes (53.3%), but in 2 eyes (13.3%) visual acuity remained at < or =20/200. In the patients with optic neuritis, the patients who showed optic neuritis at initial presentation had a worse visual prognosis (p = 0.030, Mann-Whitney U-test). CONCLUSIONS: In Korean children with multiple sclerosis, age of onset was younger than reported in other countries, and there was no female predominance. The prognosis for good visual acuity was worse in patients who initially presented with optic neuritis.