Predicting the Optical Power of Intraocular Lenses by Means of Dynamically Developing Artificial Intelligence System

The article studies the possibility of using mathematical models obtained as a result of deep learning of artificial neural networks (ANN models) to predict the optical power of state-of-the-art intraocular lenses (IOLs), widely used in the surgical cataract treatment in ophthalmology. A distinctive feature of such ANN models in comparison with the well-known formulas SRK II, SRK/T, Hoffer-Q, Holladay II, Haigis, Barrett is their ability to take into account a significant number of recorded input quantities, which makes it possible to reduce the mean relative error in calculating the optical power of IOL from 10-12% to 3.5%. ANN models have been trained on large-scale samples including depersonalized data for 455 patients. The resulting models, in contrast to the traditionally used formulas, reflect the regional specificity of patients largely. They also make it possible to retrain and optimize the structure based on newly received data taking into account the nonstationarity of the object. The results obtained allowed to build a dynamically developing artificial intelligence system for predicting the optical power of intraocular lenses with a continuous flow of new data from a source and a step-by-step retraining of the ANN model.