Virtual reality systems and deep learning tracking algorithms could be utilized in robotic control to aid in training, testing, and improving systems to reduce the risk of iatrogenic injuries.
FREMONT, CA: Artificial Intelligence (AI), particularly machine learning, is becoming more useful in ophthalmology. The use of Graphical Processing Units (GPUs) in machine learning applications has enabled recent advancements. With access to enormous volumes of data, several algorithms are now showing gains in diagnoses and even outcome prediction for many common ophthalmology conditions. Retina and glaucoma are two of the most common. Despite the potential benefits, adequate detailed and categorized data is usually unavailable for various high-interest applications.
When there is not enough data, the developer can use data analysis techniques like cross-validation, ensemble, and regularization to reduce 'overfitting,' which occurs when the algorithm does not learn despite having 'memorized' the data. Regardless of this and other analytical methods, there are instances when insufficient quality data is simply insufficient, and other instances when producing data on the required scale is not practicable.
Camera image information mixed with additional data sources such as intraoperative Optical Coherence Tomography (OCT) images, robot end-effector position, and force sensing measures could increase a robot’s ability to help or execute specific tasks during surgery in the future. The advancement of neural networks, image acquisition progress, and a considerable rise in data utilization may improve the safety and effectiveness of robotic procedures, particularly in eye surgery. Surgical viewing technologies, such as ‘heads up surgery’ systems, may enable augmented reality during retinal surgeries in addition to offering a new source of data.
Virtual reality systems and deep learning tracking algorithms could be utilized in robotic control to aid in training, testing, and improving systems to reduce the risk of iatrogenic injuries. Enhanced safety, efficacy, and dependability are potential benefits of incorporating robotics into ophthalmology, as improved data is the foundation of AI advancement. Robotic platforms are showing promise in retinal surgeries, and early human studies are encouraging. Although it seems conceivable that AI may improve these systems, the shape that such augmentation will take is still unknown. There will be several hurdles on the way to making robotics with AI’s feasibility, and humans will continue to play an important role, especially in the early stages of technology development.