BackgroundBurn infliction techniques are poorly described in rat models. An accurate study can only be achieved with wounds that are uniform in size and depth. We describe a simple reproducible method for creating consistent burn wounds in rats.MethodsTen male Sprague-Dawley rats were anesthetized and dorsum shaved. A 100 g cylindrical stainless-steel rod (1 cm diameter) was heated to 100℃ in boiling water. Temperature was monitored using a thermocouple. We performed two consecutive toe-pinch tests on different limbs to assess the depth of sedation. Burn infliction was limited to the loin. The skin was pulled upwards, away from the underlying viscera, creating a flat surface. The rod rested on its own weight for 5, 10, and 20 seconds at three different sites on each rat. Wounds were evaluated for size, morphology and depth.ResultsAverage wound size was 0.9957 cm2 (standard deviation [SD] 0.1845) (n=30). Wounds created with duration of 5 seconds were pale, with an indistinct margin of erythema. Wounds of 10 and 20 seconds were well-defined, uniformly brown with a rim of erythema. Average depths of tissue damage were 1.30 mm (SD 0.424), 2.35 mm (SD 0.071), and 2.60 mm (SD 0.283) for duration of 5, 10, 20 seconds respectively. Burn duration of 5 seconds resulted in full-thickness damage. Burn duration of 10 seconds and 20 seconds resulted in full-thickness damage, involving subjacent skeletal muscle.ConclusionsThis is a simple reproducible method for creating burn wounds consistent in size and depth in a rat burn model.

The outbreak of the coronavirus disease 2019 has further increased the urgent need for digital transformation within the health care settings, with the use of artificial intelligence/deep learning, internet of things, telecommunication network/virtual platform, and blockchain. The recent advent of metaverse, an interconnected online universe, with the synergistic combination of augmented, virtual, and mixed reality described several years ago, presents a new era of immersive and realtime experiences to enhance human-to-human social interaction and connection. In health care and ophthalmology, the creation of virtual environment with three-dimensional (3D) space and avatar, could be particularly useful in patient-fronting platforms (eg, telemedicine platforms), operational uses (eg, meeting organization), digital education (eg, simulated medical and surgical education), diagnostics, and therapeutics. On the other hand, the implementation and adoption of these emerging virtual health care technologies will require multipronged approaches to ensure interoperability with real-world virtual clinical settings, userfriendliness of the technologies and clinical efficiencies while complying to the clinical, health economics, regulatory, and cybersecurity standards. To serve the urgent need, it is important for the eye community to continue to innovate, invent, adapt, and harness the unique abilities of virtual health care technology to provide better eye care worldwide.

The rise of artificial intelligence (AI) has brought breakthroughs in many areas of medicine. In ophthalmology, AI has delivered robust results in the screening and detection of diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity. Cataract management is another field that can benefit from greater AI application. Cataract  is the leading cause of reversible visual impairment with a rising global clinical burden. Improved diagnosis, monitoring, and surgical management are necessary to address this challenge. In addition, patients in large developing countries often suffer from limited access to tertiary care, a problem further exacerbated by the ongoing COVID-19 pandemic. AI on the other hand, can help transform cataract management by improving automation, efficacy and overcoming geographical barriers. First, AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs. This utilizes a deep-learning, convolutional neural network (CNN) to detect and classify referable cataracts appropriately. Second, some of the latest intraocular lens formulas have used AI to enhance prediction accuracy, achieving superior postoperative refractive results compared to traditional formulas. Third, AI can be used to augment cataract surgical skill training by identifying different phases of cataract surgery on video and to optimize operating theater workflows by accurately predicting the duration of surgical procedures. Fourth, some AI CNN models are able to effectively predict the progression of posterior capsule opacification and eventual need for YAG laser capsulotomy. These advances in AI could transform cataract management and enable delivery of efficient ophthalmic services. The key challenges include ethical management of data, ensuring data security and privacy, demonstrating clinically acceptable performance, improving the generalizability of AI models across heterogeneous populations, and improving the trust of end-users.