Anatomical and Functional Outcomes in Delayed Onset versus Concurrent Retinal Detachment in Endophthalmitis.

Purpose: To determine the functional and anatomical outcomes of patients with endophthalmitis with concurrent or delayed onset retinal detachment (RD), and compare the preoperative, intraoperative and postoperative features. Patients and Methods: This was a retrospective review of 121 eyes in 121 patients presenting with endophthalmitis and RD. Subjects were categorized into two groups: endophthalmitis with delayed onset RD (group 1, N=76) and endophthalmitis with concurrent RD (group 2, N=45). Results: The mean age of patients in groups 1 and 2 was 38.21±21.60 and 46.78±24.42 years, respectively (P=0.047). Exogenous endophthalmitis was common in both groups 1 and 2 (86.84% and 84.44%, respectively). No significant differences were found between the groups in the type of RD, retinal breaks, number of quadrants involved or proliferative vitreoretinopathy grade. In the overall cohort, visual acuity improved post-surgery in one-third of the patients who were in the near or total blindness category at presentation. We found good anatomical success rates of an attached retina in both groups 1 and 2 (84.3% and 77.7%, P=0.376). Conclusion: Our study presents the results of patients with endophthalmitis and delayed onset RD or concurrent RD. It shows a few differences in presentation between the groups, but the anatomical and functional outcomes were almost the same.

Clinicopathological study of parasitic lesions of the eye and ocular adnexa in a tertiary care ophthalmic center in South India.

Purpose: To study clinical and pathological features of parasitic lesions in the ocular adnexa in a tertiary care ophthalmic center in south India. Methods: 43 cases of ocular parasitosis were analysed clinically and correlated with the pathological findings (gross morphology and histopathology) over a period of five years (2015-2020). Results: Among the 43 cases, the age group ranged from 9 months to 78 years (mean age of 41.6 years). Female patients were more common than male patients, with a percentage of 63% (27) and 37% (16) respectively. Cystic lesion in the lid or orbit was seen in 23 cases (53.4%); solid mass lesions were seen in 17 cases (39.5%); subconjunctival worms in three cases; and subretinal parasite in one. Gross examination and histopathologic study showed Dirofilaria in 23 cases (53.5%), followed by Cysticercus in six cases (14%) and Microfilariae in four cases (9.3%). Exact species identification was not possible in ten cases (23.25%). Correlation between the type of lesion and type of inflammatory cells with the specific parasite was done. Conclusion: Our study showed that important clinicopathological correlations can be made from the parasitic lesions in the eye and adnexa, which can aid in definitive diagnosis and prompt identification of the parasite for patient management.

Polymerase chain reaction-proven tuberculous anterior segment mass mimicking juvenile xanthogranuloma in a child.

Mass lesions arising from the anterior segment in children involving the iris and ciliary body can be of myogenic, neurogenic, or hematogenic/vascular origin. These include nevi, melanomas, adenoma, adenocarcinoma, cysts, metastatic tumours among others. Multiple iris mass lesions due to tuberculosis in children are rare. We present an uncommon atypical presentation of multiple anterior segment mass lesions referred to us as neoplasia. Although excision biopsy can be diagnostic, it was deferred and anterior chamber tap was done. Aqueous cytology was suspicious of juvenile xanthogranuloma (JXG) but polymerase chain reaction (PCR) confirmed tuberculous etiology. Treatment with antituberculous therapy (ATT) and steroids lead to complete resolution of the lesions.

Angle-Closure Detection in Anterior Segment OCT Based on Multilevel Deep Network.

Irreversible visual impairment is often caused by primary angle-closure glaucoma, which could be detected via anterior segment optical coherence tomography (AS-OCT). In this paper, an automated system based on deep learning is presented for angle-closure detection in AS-OCT images. Our system learns a discriminative representation from training data that captures subtle visual cues not modeled by handcrafted features. A multilevel deep network is proposed to formulate this learning, which utilizes three particular AS-OCT regions based on clinical priors: 1) the global anterior segment structure; 2) local iris region; and 3) anterior chamber angle (ACA) patch. In our method, a sliding window-based detector is designed to localize the ACA region, which addresses ACA detection as a regression task. Then, three parallel subnetworks are applied to extract AS-OCT representations for the global image and at clinically relevant local regions. Finally, the extracted deep features of these subnetworks are concatenated into one fully connected layer to predict the angle-closure detection result. In the experiments, our system is shown to surpass previous detection methods and other deep learning systems on two clinical AS-OCT datasets.

A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images.

PURPOSE: Anterior segment optical coherence tomography (AS-OCT) provides an objective imaging modality for visually identifying anterior segment structures. An automated detection system could assist ophthalmologists in interpreting AS-OCT images for the presence of angle closure. DESIGN: Development of an artificial intelligence automated detection system for the presence of angle closure. METHODS: A deep learning system for automated angle-closure detection in AS-OCT images was developed, and this was compared with another automated angle-closure detection system based on quantitative features. A total of 4135 Visante AS-OCT images from 2113 subjects (8270 anterior chamber angle images with 7375 open-angle and 895 angle-closure) were examined. The deep learning angle-closure detection system for a 2-class classification problem was tested by 5-fold cross-validation. The deep learning system and the automated angle-closure detection system based on quantitative features were evaluated against clinicians' grading of AS-OCT images as the reference standard. RESULTS: The area under the receiver operating characteristic curve of the system using quantitative features was 0.90 (95% confidence interval [CI] 0.891-0.914) with a sensitivity of 0.79 ± 0.037 and a specificity of 0.87 ± 0.009, while the area under the receiver operating characteristic curve of the deep learning system was 0.96 (95% CI 0.953-0.968) with a sensitivity of 0.90 ± 0.02 and a specificity of 0.92 ± 0.008, against clinicians' grading of AS-OCT images as the reference standard. CONCLUSIONS: The results demonstrate the potential of the deep learning system for angle-closure detection in AS-OCT images.