Case series of central retinal artery occlusion in COVID-19-associated rhino-orbital-cerebral mucormycosis.

Rhino orbital Mucormycosis caused by filamentous fungus of mucoraceae family was considered a rare disease affecting immunocompromised and diabetics with ketoacidosis until the recent COVID 19 pandemic. We are presenting a series of six cases of Rhino orbital cerebral Mucormycosis with central retinal artery occlusion. All six cases had common history of COVID 19 infection in recent past with sinusitis, proptosis and total ophthalmoplegia with central retinal artery occlusion on presentation. MR imaging showed invasive pan sinusitis with orbital and cerebral involvement. Urgent debridement was done and histopathological examination showed broad, filamentous aseptate fungi suggestive of Mucormycosis. All patients inspite of intravenous Amphotericin B with local debridement did not show any improvement and expired within a week of presentation. Hence our study shows poor prognosis of post covid 19 associated Mucormycosis with central retinal artery occlusion.

Comparing the efficacy of sodium hyaluronate eye drops and carboxymethylcellulose eye drops in treating mild to moderate dry eye disease.

Purpose: Aim of this study was to compare the efficacy of sodium hyaluronate (SH) and carboxymethyl cellulose (CMC) eye drops in treating mild to moderate dry eye disease in terms of relief of symptoms, mean change in tear film breakup time, Schirmer's test, and impression cytology of conjunctiva from baseline. Methods: An observational study was carried out during a 2-year period in our tertiary referral hospital. The study consisted of 60 patients randomly allotted to two groups to receive SH and CMC eye drops for an 8-week period. Ocular surface disease index, tear film breakup time, and Schirmer's test were performed at baseline visit and 4 and 8 weeks of treatment, and impression cytology of conjunctiva was performed at baseline and 8 weeks. Results: Significant improvement in patient symptoms, tear film breakup time, and Schirmer's test from baseline was seen in both SH and CMC groups at 8 weeks posttreatment, whereas impression cytology of conjunctiva in both groups did not show significant improvement at 8 weeks of treatment. Data analysis using unpaired t-test showed comparable results. Conclusion: Both CMC and SH demonstrated equal efficacy in treating mild to moderate dry eye disease.

A comparison of sub-tenon block with peribulbar block in small-incision cataract surgery.

Purpose: To compare the efficacy and safety of sub-tenon block to peribulbar block with respect to analgesia, akinesia, and complications. Methods: It is an observational study conducted at a government hospital in Karnataka. Seventy patients who came to the ophthalmology OPD for small-incision cataract surgery (SICS) under local anesthesia were included in the study. The participants were divided into two groups of 35 as per the surgeon. The pain was evaluated at the time of administration of the block, during the surgery, and during the postoperative period of 4 h. Akinesia was noted in both the groups and the time of onset of akinesia was noted. Any complications associated with the block such as chemosis or subconjunctival hemorrhage were also noted. Statistical analysis was done using PSS version 25.0, where P < 0.05 was considered significant. Results: The baseline pain score was higher in the peribulbar group (1.57). The onset of akinesia was faster in sub-tenons (90.34 s). Complete akinesia was achieved in 82.9% of patients after peribulbar block. There was no significant difference in complications in both groups. Conclusion: Sub-tenons block is an effective and safer technique of ocular anesthesia for SICS. It can be considered as an alternative to the conventional peribulbar block for SICS.

Evaluating the Viability of a Smartphone-Based Annotation Tool for Faster and Accurate Image Labelling for Artificial Intelligence in Diabetic Retinopathy.

INTRODUCTION: Deep Learning (DL) and Artificial Intelligence (AI) have become widespread due to the advanced technologies and availability of digital data. Supervised learning algorithms have shown human-level performance or even better and are better feature extractor-quantifier than unsupervised learning algorithms. To get huge dataset with good quality control, there is a need of an annotation tool with a customizable feature set. This paper evaluates the viability of having an in house annotation tool which works on a smartphone and can be used in a healthcare setting. METHODS: We developed a smartphone-based grading system to help researchers in grading multiple retinal fundi. The process consisted of designing the flow of user interface (UI) keeping in view feedback from experts. Quantitative and qualitative analysis of change in speed of a grader over time and feature usage statistics was done. The dataset size was approximately 16,000 images with adjudicated labels by a minimum of 2 doctors. Results for an AI model trained on the images graded using this tool and its validation over some public datasets were prepared. RESULTS: We created a DL model and analysed its performance for a binary referrable DR Classification task, whether a retinal image has Referrable DR or not. A total of 32 doctors used the tool for minimum of 20 images each. Data analytics suggested significant portability and flexibility of the tool. Grader variability for images was in favour of agreement on images annotated. Number of images used to assess agreement is 550. Mean of 75.9% was seen in agreement. CONCLUSION: Our aim was to make Annotation of Medical imaging easier and to minimize time taken for annotations without quality degradation. The user feedback and feature usage statistics confirm our hypotheses of incorporation of brightness and contrast variations, green channels and zooming add-ons in correlation to certain disease types. Simulation of multiple review cycles and establishing quality control can boost the accuracy of AI models even further. Although our study aims at developing an annotation tool for diagnosing and classifying diabetic retinopathy fundus images but same concept can be used for fundus images of other ocular diseases as well as other streams of medical science such as radiology where image-based diagnostic applications are utilised.