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.

Short-Term Safety and Efficacy of Intravitreal Brolucizumab Injections for Neovascular Age-Related Macular Degeneration: A Multicenter Retrospective Real-World Study.

INTRODUCTION: The aim of the study was to determine the short-term real-world safety and efficacy of intravitreal brolucizumab injections in Korean patients with neovascular age-related macular degeneration (nAMD). METHODS: This multicenter retrospective study involved 294 eyes (treatment naïve 20 eye [6.8%] and nontreatment naïve 274 eyes [93.2%]) of 290 patients from 13 hospitals or retinal centers in South Korea. Patients with nAMD who received brolucizumab injection(s) between April 1 and November 30, 2021, with a follow-up ≥1 month, were included. Primary outcomes were safety, incidence of intraocular inflammation (IOI), and potential risk factors. The secondary outcome was efficacy, i.e., change in best-corrected visual acuity (BCVA) and optical coherence tomography-measured macular thickness and retinal fluid. RESULTS: The mean age was 71.63 ± 8.66. The follow-up period was 2.38 ± 0.79 months. The mean number of brolucizumab injections during the follow-up was 1.52 ± 0.58. The overall incidence of IOI was 13.9% (n = 41 eyes). Most IOI cases were of anterior uveitis (8.8%, 26 eyes), followed by retinal vasculitis (2.4%, seven eyes) and occlusive retinal vasculitis (0.3%, one eye). Most eyes showed IOI resolution (n = 40, 97.5%) and BCVA restoration (n = 39, 95.1%) with or without corticosteroid treatment during the follow-up. Age, sex, IOI history, or other anti-vascular endothelial growth factor injection histories were not associated with the occurrence of IOI. However, only thin subfoveal choroidal thickness (SFCT) was associated with the occurrence of IOI (odds ratio = 0.995, p = 0.020). BCVA at 1 month improved from baseline (baseline 0.518 ± 0.356 vs. 1 month 0.503 ± 0.383, p = 0.023), but the improvement was not maintained. Anatomical improvement was significant after 3 months. CONCLUSION: In Korean patients with nAMD, the incidence of IOI following brolucizumab injections was 13.9%. IOI was well-controlled with or without steroid treatment. Most IOI eyes (95.1%) were restored to the level of vision before. IOI occurrence and occlusive vasculitis was rare. In the short term, brolucizumab injection effectively improved vision at 1 month and dried retinal fluid for 3 months.

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.

MACULAR HOLE ASSOCIATED WITH AGE-RELATED MACULAR DEGENERATION: Pathogenesis and Surgical Outcomes.

PURPOSE: To ascertain the pathogenesis of macular hole (MH) associated with age-related macular degeneration (AMD) and its surgical outcomes. METHODS: Patients with full-thickness MH associated with AMD (higher grades than intermediate) were enrolled. The mechanism of MH formation and closure rate after vitrectomy (surgical outcome) were determined using optical coherence tomography imaging. RESULTS: The mechanism of MH formation (35 eyes) associated with AMD was classified into four types: vitreomacular traction (42.9%), gradual retinal thinning caused by subretinal drusen or pigment epithelial detachment (22.9%), massive subretinal hemorrhage (20.0%), and combined (14.3%). In the 41 eyes that underwent vitrectomy, the logarithm of the minimum angle of resolution best-corrected visual acuity improved from 0.82 (0.10-2.30) preoperative to 0.69 (0.10-2.30) postoperative (P = 0.001). Successful closure of the MH was achieved in 33 eyes (80.5%) after vitrectomy. No significant association was observed between the closure rate of MH after vitrectomy and mechanism of MH formation (P = 0.083). CONCLUSION: The mechanism of MH formation associated with AMD was classified into four types and was not related to its surgical outcome. Considering visual improvement and surgical outcome after vitrectomy in our study, active surgical treatment can be considered for MH associated with AMD.

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.

Angulation of the inner nuclear layer as an indicator of the severity of macular pseudohole.

PURPOSE: To investigate foveal morphologic parameters related to visual acuity and the stages classified in this study reflect the severity of the macular pseudohole (MPH). METHODS: Seventy-eight eyes of 78 consecutive patients diagnosed with MPH were studied. Quantitative optical coherence tomography (OCT) parameters including central foveal thickness, parafoveal thickness, parafoveal inner and outer retinal thickness (PIRT and PORT), pseudohole depth, pseudohole diameter, and inner nuclear layer (INL) angulation were measured and the morphologic features of the inner retina (disorganization of retinal inner layers (DRIL)) and the photoreceptor layer (external limiting membrane (ELM), ellipsoid zone (EZ), interdigitation zone (IZ), and cotton ball sign) were determined. Associations between OCT parameters and best-corrected visual acuity (BCVA) were analyzed. Based on the location of the inner margin of INL, all patients were divided into three stages and the mean comparison between the three stages was analyzed. RESULTS: PIRT (r = 0.6489; p < 0.0001) and pseudohole depth (r = 0.5266; p < 0.0001) had a statistically significant correlation with BCVA. Statistically significant visual acuity differences were found in eyes with DRIL (p < 0.001) and IZ disruption (p = 0.018), but not in ELM disruption (p = 0.916), EZ disruption (p = 0.581), and cotton ball sign (p = 0.075). According to the univariate and multivariate regression analyses, PIRT was associated with BCVA in both univariate (p < 0.001) and multivariate (p = 0.002) regression analyses. Defect diameters of both ELM (p = 0.025) and IZ (p = 0.006) were associated with BCVA in univariate regression analysis, but not in multivariate regression analysis. INL angulation and the ratio of the IZ disruption was significantly different in the three groups. Stage 3 (95.8%) had significantly higher disrupted IZ ratio than stage 1 (40%) and stage 2 (65.5%). The BCVA of stages 1, 2, and 3 were identified as 0.06 ± 0.07 (20/23 Snellen equivalent), 0.23 ± 0.17 (20/34 Snellen equivalent), and 0.48 ± 0.23 (20/60 Snellen equivalent), respectively, and the differences in BCVA between the three groups were significant (p < 0.0001). CONCLUSION: The parameters related to visual acuity were PIRT, pseudohole depth, DRIL, and IZ. The stage classification proposed in this study included morphologic changes of the inner retina and photoreceptor layer and is likely to be clinically useful for showing the severity of the MPH.

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.

Anti-inflammatory Effects of Sinomenium Acutum Extract On Endotoxin-induced Uveitis in Lewis Rats.

PURPOSE: Recent studies have reported the anti-inflammatory effect of Sinomenium acutum. We investigated the anti-inflammatory effect of sinomenine on endotoxin-induced uveitis in a rat model. METHODS: Endotoxin-induced uveitis was induced in rat by lipopolysaccharide (LPS) immunization. Sinomenine (50mg/kg and 100mg/kg) was administered at 30 minutes before, 6 hours and 12 hours after LPS immunization. Clinical and histological severity was evaluated. Protein concentration and levels of tumor necrosis factor (TNF)-α and prostaglandin (PG)-E2 in aqueous humor were measured. Expression of activated Nuclear factor (NF)-κB p65 in ciliary body was also observed. RESULTS: Clinical and histological severities were significantly milder in sinomenine-treated rat than in controls (P < 0.001). Sinomenine suppressed protein leakage and down-regulated the production of TNF-α and PG-E2 in a dose-dependent manner. Sinomenine treatment suppressed the translocation of the NF-κB p65 subunit into the nuclei. CONCLUSION: Systemic administration of sinomenine suppressed the inflammation of ocular tissues. These findings suggest that sinomenine could be a novel therapeutic agent for the control of endogenous ocular inflammatory disease.

Intraocular Lens Power Calculation after Refractive Surgery: A Comparative Analysis of Accuracy and Predictability.

PURPOSE: To compare the accuracy of intraocular lens (IOL) power calculation using conventional regression formulae or the American Society of Cataract and Refractive Surgery (ASCRS) IOL power calculator for previous corneal refractive surgery. METHODS: We retrospectively reviewed 96 eyes from 68 patients that had undergone cataract surgery after keratorefractive surgeries. We calculated the formula with two approaches: IOL powers using the ASCRS IOL power calculator and IOL powers using conventional formulae with previous refractive data (Camellin, Jarade, Savini, and clinical history method) or without prior data (0, 2 and, 4 mm total mean power in topography, Wang-Koch-Maloney, Shammas, Seitz, and Maloney). Two conventional IOL formulae (the SRK/T and the Hoffer Q) were calculated with the single K and double K methods. Mean arithmetic refractive error and mean absolute error were calculated at the first postoperative month. RESULTS: In conventional formulae, the Jarade method or the Seitz method, applied in the Hoffer Q formula with the single K or double K method, have the lowest prediction errors. The least prediction error was found in the Shammas-PL method in the ASCRS group. There was no statistically significant difference between the 10 lowest mean absolute error conventional methods, the Shammas-PL method and the Barrett True-K method calculated with using the ASCRS calculator, without using preoperative data. CONCLUSIONS: The Shammas-PL formula and the Barrett True-K formula, calculated with the ASCRS calculator, without using history, were methods comparable to the 10 most accurate conventional formulae. Other methods using the ASCRS calculator show a myopic tendency.

Changes in choroidal thickness after intravitreal dexamethasone implant injection in retinal vein occlusion.

AIMS: To investigate the choroidal thickness changes in eyes with retinal vein occlusion (RVO) and to determine the effect of an intravitreal dexamethasone implant (Ozurdex) on choroidal thickness. METHODS: This study comprised 47 patients diagnosed with unilateral, RVO-associated macular oedema (MO) and treated with Ozurdex. Baseline subfoveal choroidal thickness (SFCT) and central macular thickness (CMT) of the RVO eyes were compared with those of the normal contralateral eyes; serial changes in this parameter were evaluated at 1, 3 and 5 months after Ozurdex injection. The correlation between SFCT and CMT and between visual acuity change and SFCT or CMT changes were also investigated. RESULTS: The mean SFCT was significantly higher in RVO eyes at baseline (260.3 ± 71.2 µm) than it was in contralateral eyes (217.6 ± 55.3 µm; p < 0.001). While the mean SFCT decreased at 1 and 3 months after Ozurdex injection, it slightly increased at month 5. The change in SFCT was significantly associated with the change in CMT at 3 months (r = 0.5073; p = 0.0006) and 5 months after Ozurdex injection (r = 0.3489; p = 0.0274). The improvement in visual acuity was significantly correlated with the decrease in SFCT (r = 0.3241; p = 0.0413). CONCLUSIONS: SFCT in eyes with RVO was significantly greater than it was in normal contralateral eyes. After Ozurdex injection, SFCT decreased significantly. Improvement in visual acuity was correlated with the decrease in SFCT after Ozurdex injection.

Efficacy of diagnostic monocular occlusion in revealing the maximum angle of exodeviation.

AIMS: To determine the efficacy of diagnostic monocular occlusion in revealing the maximum angle of exodeviation compared with repeated measurements taken during three or more consecutive examinations in the outpatient clinic. METHODS: We retrospectively analysed 185 patients with intermittent exotropia in an institutional referral centre. The angle of exodeviation was measured at distance and near fixation on three or more consecutive examinations in the outpatient clinic. Then 1 day of diagnostic monocular occlusion was performed and the angle of exodeviation was measured. RESULTS: After diagnostic monocular occlusion, the mean angle of deviation at distance (23.5 prism dioptres (PD)) and near fixation (23.5 PD) was significantly smaller compared with the average maximum angle of deviation before occlusion at distance (27.0 PD) and near fixation (25.2) (p=0.001, 0.022). However, 26 patients (14.1%) showed an increase of ≥5 PD in their distant angle after occlusion and 57 patients (30.8%) showed an increase of ≥5 PD in their near angle of deviation. After occlusion, 39.1% (9/23) of divergence excess (DE)-type, 20.0% (3/15) of convergence insufficiency (CI)-type and 2.7% (4/147) of basic-type exotropia were converted to other types. Patients with hyperopia were more likely to show a significant increase of ≥5 PD during near fixation. CONCLUSIONS: Diagnostic monocular occlusion could be useful in patients with DE-type or CI-type exotropia and with hyperopia. In other cases, however, it has a limited role in determining the maximum angle of exodeviation compared with multiple examinations.

Changes in choroidal thickness after systemic administration of high-dose corticosteroids: a pilot study.

PURPOSE: To characterize the effects of corticosteroids on choroidal thickness, we measured the choroid thickness in patients treated systemically with a high-dose corticosteroid. METHODS: A prospective, pilot study was conducted on 20 patients who required high-dose corticosteroid pulse therapy (>500 mg/d). Choroidal thickness was measured at baseline, 1 day, 1 week, and 1 month after corticosteroid administration. Blood pressure was measured four times a day for the first 5 days of steroid treatment. RESULTS: This study ultimately included 35 eyes from 18 patients. Each patient was treated with high-dose corticosteroid therapy at a concentration of 19.5 ± 4.1 mg per kg body weight for 5.2 ± 1.1 days. Mean subfoveal choroidal thickness at baseline was 259.8 µm (range, 86.4-394.7 µm). Choroidal thickness showed no significant change at 1 day, 1 week, or 1 month after corticosteroid administration (P = 0.197). Mean systolic blood pressure increased by 13 mmHg (P = 0.008), but diastolic pressure did not change (P = 0.117). One patient (5.6%) who had presented with pigment epithelial detatchment (PED) and thick choroid (381.1 µm) developed bilateral focal subretinal fluid during the study and showed central serous chorioretinopathy (CSC) with a 13.1% increase in subfoveal choroidal thickness. CONCLUSIONS: No consistent changes in choroidal thickness were observed after systemic high-dose corticosteroid treatment, but one patient with PED and thick choroid showed an increase in choroidal thickening as well as features of CSC. Thus, steroid-induced CSC may be an idiosyncratic response in selected vulnerable individuals rather than a dose-dependent effect.

A case of tubulointerstitial nephritis and uveitis syndrome in an elderly patient.

Tubulointerstitial nephritis and uveitis (TINU) syndrome is a rare disease entity usually occurring in children. In the present study a case of TINU syndrome in an elderly patient is described and relevant literature is reviewed. A 61-year-old man presented with bilateral flank pain, urinary frequency, and foamy urine. A kidney ultrasonography revealed an increase in kidney parenchyma echogenicity. Following a kidney biopsy, the patient was diagnosed with acute tubulointerstitial nephritis. An ophthalmology examination initially performed for floater symptoms, revealed anterior uveitis in both eyes. Acute tubulointerstitial nephritis and anterior uveitis in both eyes responded to treatment with oral prednisolone, furosemide, carvedilol, and a topical steroid. TINU syndrome can occur in the elderly and should be part of the differential diagnosis when seeing a patient who has uveitis in association with renal disease; any therapy should be managed by both an internist and an ophthalmologist.

Bilateral same-day intravitreal injections using a single vial and molecular bacterial screening for safety surveillance.

PURPOSE: To evaluate the safety of bilateral same-day intravitreal injections using a single vial and to introduce a molecular surveillance system to screen bacterial drug contamination using eubacterial polymerase chain reaction (PCR). METHODS: Retrospective review of the medical records of 135 patients who received 574 bilateral same-day intravitreal injections for various retinal diseases in 2 tertiary referral hospitals between January 2008 and March 2010 was performed. Data were obtained regarding the diagnosis, kinds of drugs injected, postinjection complications, and the result of molecular bacterial screening of the injected drugs. Drugs for bilateral intravitreal injections were drawn from a single vial and injected using separate syringes or needles. Molecular bacterial screening was performed using the remaining drug in the syringe by 16S ribosomal DNA real-time PCR. RESULTS: A total of 574 injections (384 bevacizumab, 154 ranibizumab, and 36 triamcinolone) were administered on bilateral eyes of 135 patients. There were no complications, including endophthalmitis, uveitis, retinal tear, or retinal detachment. Of the 278 injections screened for bacterial contamination using eubacterial PCR, no cases (0%) showed drug contamination by bacteria. The sensitivity of eubacterial PCR for molecular bacterial screening was 10 colony-forming units (CFUs)/mL or lower. CONCLUSION: Bilateral same-day intravitreal injections drawn from a single vial using separate syringes or needles are well tolerated by patients, and its safety profile may be equivalent to unilateral injections. The bacterial molecular surveillance system using eubacterial PCR demonstrated the safety of bilateral same-day intravitreal injections and may be used for safety surveillance and for timely intervention of possible drug-related endophthalmitis.

Presumed necrotizing viral retinitis after intravitreal triamcinolone injection: case report.

A 56-year-old man presented with anterior chamber inflammation, increased intraocular pressure, peripheral retinal infiltration, and generalized retinal arterial obstruction suggesting acute retinal necrosis five months after intravitreal triamcinolone acetonide injection (IVTA). He was treated with intravenous antiviral agents and aspirin. Shortly after treatment, retinal infiltrations were resolved, and partial recanalization of the obstructed vessel was observed. Viral retinitis may occur as an opportunistic infection following IVTA due to the local immune modulatory effect of the steroid; hence, close observation following IVTA is necessary.