The results of using machine learning technology for intraocular lenses optical power calculation

Purpose. To evaluate possibility of using the mathematical models obtained as a result of deep learning of artificial neural networks (ANNmodels) to predict the optical power of modern intraocular lenses (IOL). Material and methods. The dataset included 455 depersonalized records of patients (26 columns of input factors and one column – output factor – calculation of IOL (dptr). For convenient construction of ANN models, a simulator program previously developed by the authors and Python language tools in the Google Colaboratory were used. Results. This article describes the possibility of using mathematical models obtained as a result of deep learning of ANN models to predict the optical power of modern IOLs, widely used in the surgical cataract treatment in ophthalmology. A distinctive feature of such ANN models in comparison with the wellknown 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%. Conclusion. The resulting models, in contrast to the traditionally used formulas, reflect the regional specificity of patients to a much greater extent. They also make it possible to retrain and optimize the structure based on newly received data, which allows taking into account the non-stationarity of the object. Keywords: optical power of an intraocular lens, IOL, artificial neural networks, ANN-models, deep learning, training dataset