Optical Coherence Tomography B-scan Images Research Articles

Vitreoretinal interface abnormalities in fellow eyes of patients with a macular hole.

This study aimed to determine the incidence of bilateral macular holes (MHs), and to investigate the incidence and characteristics of retinal abnormalities in the fellow eyes, using high-definition optical coherence tomography (OCT). Medical records from 724 consecutive patients diagnosed with full-thickness MHs, who underwent surgery at Fujita Health University Hospital from May 2015 to March 2022, were retrospectively reviewed. Based on the exclusion criteria, 658 patients with idiopathic MHs were included. B-scan OCT images of the fellow eyes were used to identify vitreoretinal interface abnormalities, such as MHs, epiretinal membranes (ERMs), vitreomacular traction syndrome (VMTS) and lamellar macular holes (LMHs). The mean age of the patients was 65.9±9.0 years. There were 292 males (44.4%) and 366 females (55.6%). There were 39 fellow eyes with MHs (5.9%), 77 with ERMs (11.7%), 32 with VMTS (4.9%) and 11 with LMHs (1.7%). Thirty-four fellow eyes (5.2%) could not be classified and 469 fellow eyes were normal (71.3%). Furthermore, the 39 bilateral MHs were divided into four stages according to the Gass classification, as follows: stage 1, 32 eyes (82.1%); stage 2, none (0%); stage 3, 5 eyes (12.8%); and stage 4, 2 eyes (5.1%). Vitreoretinal interface abnormalities, such as MHs, ERMs, VMTS and LMHs, are more common in the fellow eyes of patients with MHs than in the healthy population. The fellow eyes of patients with MHs should be carefully followed using OCT.

SANS-CNN: An automated machine learning technique for spaceflight associated neuro-ocular syndrome with astronaut imaging data

Spaceflight associated neuro-ocular syndrome (SANS) is one of the largest physiologic barriers to spaceflight and requires evaluation and mitigation for future planetary missions. As the spaceflight environment is a clinically limited environment, the purpose of this research is to provide automated, early detection and prognosis of SANS with a machine learning model trained and validated on astronaut SANS optical coherence tomography (OCT) images. In this study, we present a lightweight convolutional neural network (CNN) incorporating an EfficientNet encoder for detecting SANS from OCT images titled “SANS-CNN.” We used 6303 OCT B-scan images for training/validation (80%/20% split) and 945 for testing with a combination of terrestrial images and astronaut SANS images for both testing and validation. SANS-CNN was validated with SANS images labeled by NASA to evaluate accuracy, specificity, and sensitivity. To evaluate real-world outcomes, two state-of-the-art pre-trained architectures were also employed on this dataset. We use GRAD-CAM to visualize activation maps of intermediate layers to test the interpretability of SANS-CNN’s prediction. SANS-CNN achieved 84.2% accuracy on the test set with an 85.6% specificity, 82.8% sensitivity, and 84.1% F1-score. Moreover, SANS-CNN outperforms two other state-of-the-art pre-trained architectures, ResNet50-v2 and MobileNet-v2, in accuracy by 21.4% and 13.1%, respectively. We also apply two class-activation map techniques to visualize critical SANS features perceived by the model. SANS-CNN represents a CNN model trained and validated with real astronaut OCT images, enabling fast and efficient prediction of SANS-like conditions for spaceflight missions beyond Earth’s orbit in which clinical and computational resources are extremely limited.

Quantification of choroidal hyperreflective layer: A swept-source optical coherence tomography study.

To investigate variation in reflectivity of choroidal layers in normal eyes. From the swept-source optical coherence tomography database, we retrospectively included eyes with a normal fundus. Choroidal reflectivity was measured on the horizontal and vertical B-scan optical coherence tomography images. The optical barrier of the choroid was defined as the first hill in the middle of the reflectance graph from the retinal pigment epithelium-Bruch's membrane complex to the chorioscleral junction. The optical barrier of the choroid was identified in 91 eyes of 91 individuals. The amplitude of peak reflectivity of the optical barrier of the choroid at macular center (142.85 ± 15.04) was greater than those in superior (136.12 ± 14.08) or inferior macula (135.30 ± 16.13) (P = 0.028, P = 0.008, respectively). Latency between the peak of the retinal pigment epithelium-Bruch's membrane complex and the optical barrier of the choroid at macular center (48.11 ± 13.78 μm) was shorter than those in nasal macula (55.58 ± 19.21 μm) (P = 0.021). The amplitude of the peak reflectivity of the optical barrier of the choroid in the center negatively correlated with the latency between the retinal pigment epithelium-Bruch's membrane complex and the optical barrier of the choroid (P < 0.001). An optical barrier exists in the inner choroid of the normal eye. Its depth depends on the location within the macula. Further studies are mandatory to evaluate variations in the barrier in the eyes with chorioretinal disease.

Detecting Structural Changes in the Choroidal Layer of the Eye in Neurodegenerative Disease Patients through Optical Coherence Tomography Image Processing.

To evaluate alterations of the choroid in patients with a neurodegenerative disease versus healthy controls, a custom algorithm based on superpixel segmentation was used. A cross-sectional study was conducted on data obtained in a previous cohort study. Swept-source optical coherence tomography (OCT) B-scan images obtained using a Triton (Topcon, Japan) device were compiled according to current OSCAR IB and APOSTEL OCT image quality criteria. Images were included from three cohorts: multiple sclerosis (MS) patients, Parkinson disease (PD) patients, and healthy subjects. Only patients with early-stage MS and PD were included. In total, 104 OCT B-scan images were processed using a custom superpixel segmentation (SpS) algorithm to detect boundary limits in the choroidal layer and the optical properties of the image. The algorithm groups pixels with similar structural properties to generate clusters with similar meaningful properties. SpS selects and groups the superpixels in a segmented choroidal area, computing the choroidal optical image density (COID), measured as the standard mean gray level, and the total choroidal area (CA), measured as px2. The CA and choroidal density (CD) were significantly reduced in the two neurodegenerative disease groups (higher in PD than in MS) versus the healthy subjects (p < 0.001); choroidal area was also significantly reduced in the MS group versus the healthy subjects. The COID increased significantly in the PD patients versus the MS patients and in the MS patients versus the healthy controls (p < 0.001). The SpS algorithm detected choroidal tissue boundary limits and differences optical density in MS and PD patients versus healthy controls. The application of the SpS algorithm to OCT images potentially acts as a non-invasive biomarker for the early diagnosis of MS and PD.

Evaluation of epiretinal membrane formation after scleral buckling for treating rhegmatogenous retinal detachment: En face optical coherence tomography image-based study.

To assess epiretinal membrane (ERM) formation, severity, and the associated risk factors after scleral buckling using en face optical coherence tomography (OCT) images. Medical records of 61 consecutive patients (66 eyes) with rhegmatogenous retinal detachment who underwent scleral buckling were retrospectively reviewed. Posterior vitreous detachment (PVD) was determined based on B-scan OCT images. En face OCT images were used to visualize the ERM and retinal folds. ERM formation was identified by comparing en face images pre- and post-surgery. The maximum depth of the retinal folds (MDRF) was measured using en face imaging to objectively assess traction strength. ERM formation occurred in 15 (22.7%) eyes at the final visit; the foveal pit was preserved in all cases. Parafoveal retinal folds were present in 5 (7.6%) eyes, with a mean MDRF of 21.8 ± 12.6µm. No significant difference was observed in best-corrected visual acuity (logarithm of the minimal angle of resolution) between the ERM formation (-0.019 ± 0.128) and non-ERM formation (-0.001 ± 0.213) groups at the final visit (P = 0.593; Mann-Whitney U test). Multivariate logistic regression analysis revealed that older age and the presence of PVD were significant risk factors for ERM formation (odds ratio 1.07, 95% confidence interval 1.01-1.14, P = 0.032; odds ratio 5.26, 95% confidence interval 1.06-26.10, P = 0.042; respectively). ERM occurred in 22.7% of cases but was mild and did not affect visual acuity. Older age and the presence of PVD are risk factors for ERM formation.

Diagnostic value of 3D Optical Coherence Tomography Multimode Images in the Diagnosis of Acute Central Serous Chorioretinopathy.

Spectral-Domain Optical Coherence Tomography (SD-OCT) provides non-invasive, high-speed, high-resolution, three-dimensional cross-section imaging of the macula. This study aimed to investigate the diagnostic value of the multimodal imaging technique of three-dimension (3D) optical coherence tomography (OCT) (3D-OCT) for the diagnosis and characterization of acute central serous chorioretinopathy (CSC). In this prospective clinical study 3D-OCT examinations of 82 cases with acute CSC were performed on the macular area, and the image characteristics were analyzed. Our study included a total of 87 eyes from 82 cases of CSC patients, 67 males and 15 females (mean age ± standard deviation (SD): 42.89 ±7.80 years old; age range: 27 to 56 years old. The 3D-OCT images were evaluated for the presence of subretinal fluid, subretinal space, fluctuation of the internal limiting membrane (ILM), folds of retinal pigment epithelial (RPE), retinal pigment epithelium detachment (PED), and flat irregular PED. The foveal thickness was measured using the manual caliper of OCT software. The OCT B-scan images showed 87 (100%) eyes had exudative retinal detachment (ERD), 38 (44%) had flat irregular PED, 36 (41%) had PED, 8 (9%) had subretinal turbidity structure, 2 (2%) had subretinal dot-like precipitates, 1 (1%) had focal choroidal excavation (FCE), and 1 (1%) eye had fluctuation of internal limiting membrane (FI). In the ILM-RPE thickness map, all eyes had a round or round like regular uniform domes. Fifty-seven (66%) domes were limited in the examination area and 30 (44%) domes were beyond the scope of this examination and only a partial section of the dome could be observed. In the en-face image, all eyes had a round or round-like black figure that corresponded with domes in the ILM-RPE thickness map. In RPE surface, 76 (87%) eyes had a shallow plate depression, 71(82%) had small focal uplift, and 1 (1%) eye had a focal concave feature. In the OCT ILM-RPE thickness, en-face image, and RPE surface maps, acute CSC exhibited specific imaging characteristics that can be helpful for reliable diagnosis and differential diagnosis of CSC.

Poster Session: Detecting spaceflight associated neuro-ocular syndrome (SANS) using light-weight convolutional neural networks.

Spaceflight-associated neuro-ocular syndrome (SANS) is a collection of neuro-ophthalmic findings that occurs in astronauts as a result of prolonged microgravity exposure in space. Due to limited resources on board long-term spaceflight missions, early disease diagnosis and prognosis of SANS become unviable. Moreover, the current retinal imaging techniques onboard the international space station (ISS), such as optical coherence tomography (OCT), ultrasound imaging, and fundus photography, require an expert to distinguish between SANS and similar ophthalmic diseases. With the advent of Deep Learning, diagnosing diseases (such as diabetic retinopathy) from structural retinal images are being automated. In this study, we propose a lightweight convolutional neural network incorporating an EfficientNet encoder for detecting SANS from OCT images. We used 6303 OCT B-scan images for training/validation (80%/20% split) and 945 for testing. Our model achieved 84.2% accuracy on the test set, i.e., 85.6% specificity, and 82.8% sensitivity. Moreover, it outperforms two other state-of-the-art pre-trained architectures, ResNet50-v2 and MobileNet-v2, by 21.4% and 13.1%. Additionally, we use GRAD-CAM to visualize activation maps of intermediate layers to test the interpretability of our model's prediction. The proposed architecture enables fast and efficient prediction of SANS-like conditions for future long-term spaceflight mission in which computational and clinical resources are limited.

Quantitative analysis of choroidal morphology in preeclampsia during pregnancy according to retinal change

We sought to investigate changes in choroidal hemodynamics in preeclampsia according to presence of retinal change by quantitatively assessing choroidal vessels using optical coherence tomography (OCT). This retrospective study included 106 eyes (of 53 patients) with preeclampsia, including 70 eyes without retinal change in patients with preeclampsia (Group A), 22 eyes with retinal change in patients with preeclampsia (Group B), and 14 eyes of normal pregnant women (controls). Subfoveal choroidal thickness (SFCT) was measured on OCT B-scan images, and choroidal vessel densities (CVDs) were calculated using binarized OCT B-scan and en face images. Their values were then correlated with clinical parameters associated with preeclampsia. SFCT was greater in Group B compared to Group A or controls (354.32 ± 65.13 vs. 288 ± 55.68 or 277.21 ± 50.08, both P < 0.001). CVD on B-scan images was greater in Group B compared to Group A or controls (76.4 ± 4.9 vs. 73.7 ± 5.3 or 71.5 ± 5.1; both P ≤ 0.046). CVD on en face images was also greater in Group B compared to Group A or controls (64.7 ± 0.8 vs. 63.6 ± 1.5 or 63.3 ± 1.3; both P ≤ 0.001). SFCT and CVD positively correlated with each other (P ≤ 0.009) and were greater in patients with blurred vision and vaginal bleeding (P ≤ 0.020 for blurred vision and P ≤ 0.024 for vaginal bleeding). SFCT and CVDs were higher in preeclampsia patients with retinal change compared to those without retinal change or controls. Both SFCT and CVD showed association with blurred vision and vaginal bleeding.

Generating OCT B-Scan DME images using optimized Generative Adversarial Networks (GANs)

Diabetic Macular Edema (DME) represents a significant visual impairment among individuals with diabetes, leading to a dramatic reduction in visual acuity and potentially resulting in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that produces high-resolution retinal images, plays a vital role in the clinical assessment of this condition. Physicians typically rely on OCT B-Scan images to evaluate DME severity. However, manual interpretation of these images is susceptible to errors, which can lead to detrimental consequences, such as misdiagnosis and improper treatment strategies. Hence, there is a critical need for more reliable diagnostic methods. This study aims to address this gap by proposing an automated model based on Generative Adversarial Networks (GANs) to generate OCT B-Scan images of DME. The model synthesizes images from patients' baseline OCT B-Scan images, which could potentially enhance the robustness of DME detection systems. We employ five distinct GANs in this study: Deep Convolutional GAN, Conditional GAN, CycleGAN, StyleGAN2, and StyleGAN3, drawing comparisons across their performance. Subsequently, the hyperparameters of the best-performing GAN are fine-tuned using Particle Swarm Optimization (PSO) to produce more realistic OCT images. This comparative analysis not only serves to improve the detection of DME severity using OCT images but also provides insights into the appropriate choice of GANs for the effective generation of realistic OCT images from the baseline OCT datasets.

Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids.

Diabetic Macular Edema (DME) is a severe ocular complication commonly found in patients with diabetes. The condition can precipitate a significant drop in VA and, in extreme cases, may result in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that yields high-resolution retinal images, is often employed by clinicians to assess the extent of DME in patients. However, the manual interpretation of OCT B-scan images for DME identification and severity grading can be error-prone, with false negatives potentially resulting in serious repercussions. In this paper, we investigate an Artificial Intelligence (AI) driven system that offers an end-to-end automated model, designed to accurately determine DME severity using OCT B-Scan images. This model operates by extracting specific biomarkers such as Disorganization of Retinal Inner Layers (DRIL), Hyper Reflective Foci (HRF), and cystoids from the OCT image, which are then utilized to ascertain DME severity. The rules guiding the fuzzy logic engine are derived from contemporary research in the field of DME and its association with various biomarkers evident in the OCT image. The proposed model demonstrates high efficacy, identifying images with DRIL with 93.3% accuracy and successfully segmenting HRF and cystoids from OCT images with dice similarity coefficients of 91.30% and 95.07% respectively. This study presents a comprehensive system capable of accurately grading DME severity using OCT B-scan images, serving as a potentially invaluable tool in the clinical assessment and treatment of DME.

Post-treatment Visual Acuity Prediction Using Deep Learning in Age-related Macular Degeneration

Purpose: To develop a deep learning model to predict visual acuity (VA) outcomes after 12 months of anti-vascular endothelial growth factor (anti-VEGF) treatment.Methods: A total of 330 treatment-naive eyes of neovascular age-related macular degeneration patients, who underwent anti-VEGF therapy between 2007 and 2020 at Konkuk University medical center, were included. The network was trained using VA at baseline, VA after three loading doses of anti-VEGF, and treatment regimen data. It was also trained using 12,300 augmented optical coherence tomography (OCT) B-scan images at baseline and after three loading doses of anti-VEGF. We generated five deep learning models using sequentially input data (VA and OCT B-scan images at baseline and after three loading doses, and treatment regimen). Prediction of VA at 12 months was performed using deep learning algorithms, such as convolutional neural network and multilayer perceptron. The outcomes were dichotomized based on whether the decremental change in VA during the 12 months of treatment was more or less than logarithm of the minimum angle of resolution 0.3. Predictive efficiency was assessed by comparing the performance of deep learning models.Results: The best performing model was trained using input data, including VA at baseline and after three loading doses, treatment regimen, and OCT B-scan images at baseline and after three loading doses. The decremental outcome in VA after 12 months of anti-VEGF treatment was predicted as an area under the curve (AUC) of 0.79. The addition of OCT images at baseline and after three loading doses as input data improved the AUC, sensitivity, and negative predictive value (AUC 0.74-0.79, 0.58-0.86, and 0.90-0.95, respectively).Conclusions: Our deep learning model showed relatively good performance in classifying good or poor post-treatment VA based on combined clinical information including numerical and image data.

Quantitative Analysis of Choroidal Morphology Using Multimodal Imaging in Acute and Persistent Central Serous Chorioretinopathy.

To analyze quantitative differences in choroidal morphology between acute and persistent central serous chorioretinopathy (CSC) using multimodal images. Ultra-widefield indocyanine green angiography (UWICGA) and optical coherence tomography (OCT) images of 72 eyes of 72 patients with acute (32 eyes) and persistent (40 eyes) CSC were collected. Choroidal thickness, area, vessel density, symmetry, and intervortex anastomosis were assessed. The choroidal area on OCT B-scan images was smaller and the choroidal vessel density on UWICGA images was lower in the persistent group (P < 0.001 and P = 0.028, respectively). Choroidal vessel density on UWICGA showed positive correlation with that of vortex ampullae (all P ≤ 0.046). The constitution of the intervortex anastomosis and dominant vessels in the macular area showed differences between the groups (P = 0.014 and P = 0.010, respectively), with greater inferonasal vessel participation in the anastomosis and combined supero- and inferotemporal vessels as dominant vessels in the persistent groups. Acute and persistent CSC differed in subfoveal choroidal area, choroidal vessel density, intervortex anastomosis constitution on UWICGA images. Choroidal vessel density on UWICGA images correlated with that of vortex ampullae. These findings enhance our understanding of the pathophysiology of CSC subtypes.

Wayfinding artificial intelligence to detect clinically meaningful spots of retinal diseases: Artificial intelligence to help retina specialists in real world practice.

To aim of this study is to develop an artificial intelligence (AI) that aids in the thought process by providing retinal clinicians with clinically meaningful or abnormal findings rather than just a final diagnosis, i.e., a "wayfinding AI." Spectral domain optical coherence tomography B-scan images were classified into 189 normal and 111 diseased eyes. These were automatically segmented using a deep-learning based boundary-layer detection model. During segmentation, the AI model calculates the probability of the boundary surface of the layer for each A-scan. If this probability distribution is not biased toward a single point, layer detection is defined as ambiguous. This ambiguity was calculated using entropy, and a value referred to as the ambiguity index was calculated for each OCT image. The ability of the ambiguity index to classify normal and diseased images and the presence or absence of abnormalities in each layer of the retina were evaluated based on the area under the curve (AUC). A heatmap, i.e., an ambiguity-map, of each layer, that changes the color according to the ambiguity index value, was also created. The ambiguity index of the overall retina of the normal and disease-affected images (mean ± SD) were 1.76 ± 0.10 and 2.06 ± 0.22, respectively, with a significant difference (p < 0.05). The AUC used to distinguish normal and disease-affected images using the ambiguity index was 0.93, and was 0.588 for the internal limiting membrane boundary, 0.902 for the nerve fiber layer/ganglion cell layer boundary, 0.920 for the inner plexiform layer/inner nuclear layer boundary, 0.882 for the outer plexiform layer/outer nuclear layer boundary, 0.926 for the ellipsoid zone line, and 0.866 for the retinal pigment epithelium/Bruch's membrane boundary. Three representative cases reveal the usefulness of an ambiguity map. The present AI algorithm can pinpoint abnormal retinal lesions in OCT images, and its localization is known at a glance when using an ambiguity map. This will help diagnose the processes of clinicians as a wayfinding tool.

An Automatic Image Processing Method Based on Artificial Intelligence for Locating the Key Boundary Points in the Central Serous Chorioretinopathy Lesion Area.

Accurately and rapidly measuring the diameter of central serous chorioretinopathy (CSCR) lesion area is the key to judge the severity of CSCR and evaluate the efficacy of the corresponding treatments. Currently, the manual measurement scheme based on a single or a small number of optical coherence tomography (OCT) B-scan images encounters the dilemma of incredibility. Although manually measuring the diameters of all OCT B-scan images of a single patient can alleviate the previous issue, the situation of inefficiency will thus arise. Additionally, manual operation is subject to subjective factors of ophthalmologists, resulting in unrepeatable measurement results. Therefore, an automatic image processing method (i.e., a joint framework) based on artificial intelligence (AI) is innovatively proposed for locating the key boundary points of CSCR lesion area to assist the diameter measurement. Firstly, the initial location module (ILM) benefiting from multitask learning is properly adjusted and tentatively achieves the preliminary location of key boundary points. Secondly, the location task is formulated as a Markov decision process, aiming at further improving the location accuracy by utilizing the single agent reinforcement learning module (SARLM). Finally, the joint framework based on the ILM and SARLM is skillfully established, in which ILM provides an initial starting point for SARLM to narrow the active region of agent, and SARLM makes up for the defect of low generalization of ILM by virtue of the independent exploration ability of agent. Experiments reveal the AI-based method which joins the multitask learning, and single agent reinforcement learning paradigms enable agents to work in local region, alleviating the time-consuming problem of SARLM, performing location task in a global scope, and improving the location accuracy of ILM, thus reflecting its effectiveness and clinical application value in the task of rapidly and accurately measuring the diameter of CSCR lesions.

New artificial intelligence analysis for prediction of long-term visual improvement after epiretinal membrane surgery.

To predict improvement of best corrected visual acuity(BCVA) 1 year after pars plana vitrectomy (PPV) for epiretinal membrane(ERM) using artificial intelligence(AI) methods on optical coherence tomography(OCT) B-scan images. Four-hundred eleven(411) patients with stage II ERM were divided in a group improvement(IM)(≥ 15 ETDRS letters of VA recovery)and a group no improvement(N-IM)(<15 letters)according to 1-year VA improvement after 25 G PPV with internal limiting membrane(ILM) peeling.Primary outcome was the creation of a deep learning classifier(DLC) based on OCT B-scan images for prediction.Secondary outcome was assessment of the influence of various clinical and imaging predictors on BCVA improvement. Inception-ResNet-V2 was trained using standard augmentation techniques.Testing was performed on an external dataset.For secondary outcome, B-scan acquisitions were analyzed by graders both before and after fibrillary changes(FC) processing-enhancement. The overall performance of the DLC showed a sensitivity of 87.3% and a specificity of 86.2%. Regression analysis showed a difference in preoperative images prevalence of ectopic inner foveal layer (EIFL),foveal detachment,ellipsoid zone(EZ) interruption, cotton wool sign, unprocessed FC(OR=2.75(CI 2.49-2.96)) and processed FC(OR=5.42(CI 4.81-6.08)) while preoperative BCVA and central macular thickness(CMT) didn't differ between groups. The DLC showed high performances in predicting 1-year visual outcome in ERM surgery patients. FC should also be considered as relevant predictors.

Reduced Size of Telangiectatic Capillaries After Intravitreal Injection of Anti-Vascular Endothelial Growth Factor Agents in Diabetic Macular Edema

Intravitreal injection of anti-vascular endothelial growth factor (anti-VEGF) agents reduces microaneurysms in patients with diabetic macular edema (DME). However, residual anti-VEGF-resistant telangiectatic capillaries (TelCaps) have been reported. In this study, we investigated changes in the size of TelCaps after intravitreal injection of anti-VEGF agents in DME. Indocyanine green angiography (IA) and optical coherence tomography were performed before and 3 months after the intravitreal injection of anti-VEGF agents (pro re nata regimen after three monthly loading doses) in 12 eyes of 12 patients (7 males and 5 females, mean age 65.2 ± 8.8 years) with DME. The number and size of TelCaps within a 6-mm diameter macular region of the edema were measured using optical coherence tomography B-scan images overlaid on IA images. There were significant reductions in the number and size of TelCaps between the baseline and 3 months after anti-VEGF agent administration (P < 0.05 and P < 0.0001, respectively). The maximum corrected visual acuity (logMAR visual acuity) and the central macular thickness after anti-VEGF therapy were significantly improved (P < 0.01 and P < 0.02, respectively). The TelCaps remaining after loading three consecutive anti-VEGF agents had a significantly larger mean size at baseline than the TelCaps that resolved after the treatment (P < 0.03). Our study demonstrated that intravitreal injection of anti-VEGF agents could reduce TelCap size in patients with DME. We propose that larger-sized TelCaps detected by IA might be useful predictors of refractory DME, which could thus be principal targets of laser photocoagulation.

Birefringence-derived scleral artifacts in optical coherence tomography images of eyes with pathologic myopia

We investigated birefringence-derived scleral artifacts in optical coherence tomography (OCT) images of eyes with pathologic myopia. This study included 76 eyes of 42 patients with pathologic myopia. Five sets of OCT B-scan images of the macula were obtained using commercial swept-source OCT. A dataset of prototype swept-source polarization-diversity OCT images was used to identify polarization-dependent OCT images (i.e., complex averaging of OCT signals from two polarization channels) and polarization-independent OCT images (i.e., intensity averaging of two OCT signals). Polarization-dependent OCT images and commercial OCT images were assessed for the presence of birefringence-derived artifacts by comparison with polarization-independent OCT images. Both polarization-dependent OCT images and commercial OCT images contained scleral vessel artifacts. Scleral vessel artifacts were present in 46 of 76 eyes (60.5%) imaged by polarization-dependent OCT and 17 of 76 eyes (22.4%) imaged by commercial OCT. The proportion of images that showed scleral vessel artifacts was significantly greater among polarization-dependent OCT images than among commercial OCT images (P < 0.001). Additionally, polarization-dependent OCT images showed low-intensity band artifacts. This study demonstrated the existence of birefringence-derived scleral artifacts in commercial OCT images and indicated that polarization-diversity OCT is an effective tool to evaluate the presence of these artifacts.

A curriculum learning-based fully automated system for quantification of the choroidal structure in highly myopic patients

Objective. An automated tool for choroidal segmentation and quantitative analysis under pathological conditions is currently lacking, hindering the exploration of choroidal structural changes in fundus diseases. This study aims to create a fully automated deep learning system for the quantitative analysis of the choroid with pathological changes, and to apply the system in analyzing the correlation between the choroidal structure and the severity of high myopia. Approach. A total of 2590 optical coherence tomography B-scan images of 1424 eyes of 1029 patients of high myopia from 3 hospitals were collected. We developed a curriculum learning-based system, including a two-stage U-net (TSU-net) and a post-process module for segmentation of the choroid, to calculate mean choroidal thickness (MCT) and choroidal vascularity index (CVI). The output of the images was statistically analyzed to explore the associations among MCT, CVI and the clinical characteristics of the patients. Main results. The Dice coefficient and IoU measures of choroid segmentation were 0.9221 and 0.8575, respectively. In a human-machine comparison, the system performed faster and better than a senior ophthalmologist. Statistical analysis demonstrated that, MCT is correlated with age, scan region, axial length, maculopathy type, and CVI, and CVI is correlated with scan region and MCT. Significance. A fully automated choroidal structural quantification system was developed. Clinical evaluation demonstrated that severity of high myopia is closely related to MCT but shows only a low correlation with CVI, suggesting that CVI may have little applicability in eyes with large anatomical structural variations. Future quantitative analysis of choroidal structure of large samples will enable exploration of the pathogenesis of additional fundus diseases.

UNSUSPECTED CENTRAL VISION DECREASE FROM MACULAR GANGLION CELL LOSS AFTER POSTERIOR SEGMENT SURGERY.

To describe a novel postsurgical complication of circumscribed macula ganglion cell loss as an apparent cause of unsuspected vision loss following posterior segment surgery. Patients were seen in referral and were evaluated with comprehensive examination to include optical coherence tomography and microperimetry. All eight patients had a sudden central vision loss following vitrectomy, and many patients suspected the central loss was present even before the eye patch was removed on the first postoperative day. Of the eight patients, one had a vitrectomy with no membrane peeling, whereas the remaining seven had membrane peeling. The mean postoperative visual acuity was 20/200. The mean ganglion cell layer volume was 0.69 µm3 in the involved eye and 1.035 µm3 in the fellow eye (P < 0.001). The global retinal nerve fiber layer thicknesses in the involved and fellow eyes were 81.3 and 90 µm, respectively (P = 0.08). The outer retinal architecture was unremarkable in the involved eyes and did not appear to explain the poor acuity. The ganglion cell layer volume loss was not necessarily associated with retinal nerve fiber layer thicknesses that were in the abnormal range or in optic nerve pallor. Microperimetry showed severe depression of the threshold sensitivities. Severe loss of the macular ganglion cells may occur after vitrectomy and is associated with central vision loss. The diagnosis is made by having a high index of suspicion when examining optical coherence tomography B-scan images and by evaluating ganglion cell layer volumes. The frequency of this occurrence is currently unknown.

Morphologic Changes in the Lamina Cribrosa Upon Intraocular Pressure Lowering in Patients With Normal Tension Glaucoma.

PurposeThe purpose of this study was to investigate whether the lamina cribrosa (LC) curve changes in response to intraocular pressure (IOP) reduction following administration of topical ocular hypotensive eye drops in eyes with normal tension glaucoma (NTG).MethodsNinety-three eyes of 93 patients with treatment naïve NTG at initial examination and with ≥20% reduction from baseline IOP following administration of topical ocular hypotensive eye drops were included. Serial horizontal B-scan images of the optic nerve head (ONH) were obtained from each eye using enhanced depth imaging spectral domain optical coherence tomography (OCT) before and 1 year after IOP-lowering treatment. The LC curvature in each eye was assessed by measuring the LC curvature index (LCCI) in horizontal OCT B-scan images obtained at three (superior, central, and inferior) locations spaced equidistantly across the vertical optic disc diameter before and after IOP-lowering treatment. We evaluated the OCT detectible change in the LC curvature based on the interval change of LCCI to exceed the intersession standard deviation of 1.96 times and factors associated with the magnitude of the LCCI change in the eyes showing significant LC change.ResultsIOP decreased from 15.7 ± 2.5 mm Hg at baseline to 11.2 ± 1.7 mm Hg after topical glaucoma medication. Among the 93 subjects, 62 (66.7%) eyes showed the significant reduction of the LCCI (interssetional change over 1.5) after the treatment; greater interssessional change of the LCCI after IOP reduction was associated with younger age (P = 0.020) and larger baseline LCCI (P < 0.001).ConclusionsThe OCT detectible changes in LC curvature occurred in response to a modest decrease in the IOP in the naïve NTG eyes. The therapeutic benefit of these changes need to be assessed in longitudinal studies.