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Extended reality is one of the leading cutting-edge technologies, which has not yet fully set foot into the field of ophthalmology. The use of extended reality technology especially in ophthalmic education and counseling will revolutionize the face of teaching and counseling on a whole new level. We have used this novel technology and have created a holographic museum of various anatomical structures such as the eyeball, cerebral venous system, cerebral arterial system, cranial nerves, and various parts of the brain in fine detail. These four-dimensional (4D) ophthalmic holograms created by us (patent pending) are cost-effectively constructed with TrueColor confocal images to serve as a new-age immersive 4D pedagogical and counseling tool for gameful learning and counseling, respectively. According to our knowledge, this concept has not been reported in the literature before.

Purpose: For diagnosing glaucomatous damage, we have employed a novel convolutional neural network (CNN) from TrueColor confocal fundus images to conquer the black box dilemma in artificial intelligence (AI). This neural network with CNN architecture with human-in-the-loop (HITL) data annotation helps not only in diagnosing glaucoma but also in predicting and locating detailed signs in the glaucomatous fundus, such as splinter hemorrhages, glaucomatous optic atrophy, vertical glaucomatous cupping, peripapillary atrophy, and retinal nerve fiber layer (RNFL) defect. Methods: The training was done on a well-curated private dataset of 1,400 high-resolution confocal fundus images, out of which 1,120 images (80%) were used exclusively for training and 280 images (20%) were used exclusively for testing. A custom trained You Only Look Once version 5 (YOLOv5)-based object detection methodology was used to identify the underlying conditions precisely. Twenty-six predefined medical conditions were annotated by a team of humans (comprising two glaucoma specialists and two optometrists) by using the Microsoft Visual Object Tagging Tool (VoTT) tool. The 280 testing images were split into three groups (90,100, and 90 images) for three test runs done once every 15 days. Results: Test results showed consistent increments in the accuracy, from 94.44% to 98.89%, in predicting the glaucoma diagnosis along with the detailed signs of the glaucomatous fundus Conclusion: Utilizing human intelligence in AI for detecting glaucomatous fundus images by using HITL machine learning has never been reported in the literature before. This AI model not only has good sensitivity and specificity in accurate glaucoma predictions but is also an explainable AI, thus overcoming the black box dilemma.

Summary Objectives To assess the socio‐demographic profile, pattern and treatment outcomes of pesticides poisoning. Methods A prospective observational study was conducted at the department of emergency medicine of a South Indian tertiary care hospital for 1.5 years to study the pattern and outcomes of poisoning cases due to pesticides. Level of significance (P) <0.05 was considered as statistically significant. Results A total of 375 poisoning victims with intentional/accidental exposure to pesticides were followed up and documented. The male–female ratio was 1:0.32; mean age was 31.65 ± 13.10 years. 72% of cases were rural residents. Organophosphorus compounds were the most implicated pesticides. Mean Glasgow Comatose Score (GCS) of the patients was 12.22 ± 3.86. 80.3% of patients recovered while 6.4% died. About 13.3% patients were lost to follow‐up as they were discharged against medical advice (DAMA). Conclusion There was a statistical significance seen in the implication of pesticides for intentional poisoning with age, route of administration, area of residence and occupation of the victims. However, there was a strong association of the outcomes of poisoning with the toxic agent implicated for the poisoning.