ABSTRACT
Background: The field of ophthalmology has been built upon continuous innovations. COVID?19 pandemic has been an important driving force behind many innovations in ophthalmology and other branches of medicine. Innovations in ophthalmology has been a key to surgical progress. The process of promoting innovation in surgery is imperative in the evolving practice of ophthalmology. Purpose: In this video we demonstrate some incremental innovations in operation theaters which help in increasing the efficiency and improving the performance of a surgeon. These innovations also provide a more comfortable environment for the patient undergoing the surgery. Synopsis: A few incremental innovations that are described in our video also help in preventing the spread of COVID infection during surgery. This video also showcases a few wet lab innovations that help train residents in their surgical skills. Highlights: Use and reuse of simple materials make it cost effective and ecofriendly. These incremental innovations help in the smooth running of operation theaters. Thus, they are small improvements in the existing setup and help in creating a smooth and error free OT flow
ABSTRACT
Background: Artificial Intelligence (AI) is an area of computer science that encompasses the creation of intelligent machines that work and react like humans. It deals with the development algorithms that seek to simulate human brain and also mimic cognitive functions typically associated with the human mind such as learning and problem solving. Purpose: Do we need artificial intelligence in Glaucoma? Glaucoma is the second most common cause of blindness in the world. Its prevalence was over 60 million in 2010 and over 80 million by 2020. It is so common, yet so easily overlooked. More importantly, about 50% of patients in developed countries and 90% in developing countries are unaware of having glaucoma. Early detection can delay the progression of glaucoma. Hence the time is ripe to advovate glaucoma screening. Synopsis: The application of AI in ophthalmology mainly concentrates on the diseases with a high incidence, such as diabetic retinopathy, age-related macular degeneration, glaucoma, retinopathy of prematurity, age-related or congenital cataract etc AI involves mainly 1. machine learning that are algorithms with the ability to learn without being explicitly programmed and 2. deep learning in which artificial neural networks adapt and learn from vast amounts of data. But there are limitations to screening - such as disparity between ophthalmologist:patient ratio and also the availability of the specialty services. The large amount of data acquired from patients makes it nearly impossible for ophthalmologists to screen them with equal efficacy and consistency. Highlights: AI in glaucoma aims at including factors such as clinical data, genomic data, life style behaviors, risk factors, and medical history to predict the risk of developing glaucoma, help customise the most appropriate management protocol for a given patient, and estimate prognosis and surgical success.
ABSTRACT
Purpose: To assess the diagnostic accuracy of forced eyelid closure test (FECT), ice pack test (IPT), repetitive nerve stimulation test (RNS), and acetylcholine receptor (AchR) antibody test in patients with suspected ocular myasthenia. To assess the clinical utility of AchR antibody test in predicting disease progression. Methods: Retrospective cohort study of patients diagnosed with ocular myasthenia at a South?Indian neuro?ophthalmology tertiary eye clinic. Baseline characteristics; ocular myasthenia symptoms; results of FECT, IPT, RNS, and AchR antibody test; and progression time to generalized myasthenia (GM) over 36 months from the time of diagnosis were recorded and analyzed using receiver operator curve analysis, multiple logistic regression, and Kaplan朚eier survival analysis. Results: FECT had a sensitivity of 96.7% (95% CI: 88.5�.6) and a specificity of 75% (95% CI: 34.9�.8). Combination of FECT and IPT, using the positivity of at least one test, increased the sensitivity to 98.3% (95% CI: 91�0), reducing the specificity to 50% (95% CI: 15.7�.3), whereas using the positivity of both tests, we obtained a sensitivity of 71.7% (95% CI: 58.6�.5) and a specificity of 100% (95% CI: 63.1�0). In the subset of patients with double negative RNS and AchR antibodies, the positive predictive value of combined FECT and IPT (double positive) was 100%. Patients who developed GM were more likely to have a positive AchR antibody test result (P = 0.001). Conclusion: Combined FECT and IPT (double positive) has high diagnostic accuracy even among patients with normal RNS and negative AchR antibodies. Despite low sensitivity, AchR?antibody test has a significant predictive value in disease progression.