The models of artificial neural network for intraocular lens power calculation. Comparison with fourth-generation formulas

Purpose of the study — to develop a model for preoperative calculation of the optical power of intraocular lenses (IOLs) based on artificial neural networks (ANN model) with open architecture, its machine learning on local empirical data and comparison of the model error with modern fourth-generation formulas.

Materials and methods. The initial dataset included anonymized data of patients of S.N. Fedorov Tambov brahch of National medical research center “MNTK Eye Microsurgery”, and contained 890 records, including refraction of the strong and weak cornea meridians before surgery, axial length, anterior chamber depth, lens thickness, and A-constant of the IOL model used. The required optical power of the IOL was selected as the output value. To develop ANN models, standard machine learning tools of the Python language were used, as well as gradient and gradient-free methods of the author’s development, which were used in interactive mode. All technological processes were carried out in Google Colaboratory. To assess the quality of ANN models, we used the average relative error and the percentage of calculated values falling within the target range of ±0.5 D.

Results. The accuracy of fourth-generation formulas used for preoperative calculation of the optical power of IOLs — Barrett Universal II, Hill-RBF, Kane and Pearl DGS was assessed using a significant amount of local data. The average relative error is 2.67–3.21 %, the percentage of calculated values falling within the range of ±0.5 D is from 55 to 68 %. An ANN model based on machine learning has been developed, which allows calculating this indicator with an error of 2.33 %, with the percentage of calculated values falling within the target range of about 74 %.

Conclusion. The developed ANN model can be used in decision support systems for ophthalmologists in the form of a specialized calculator.

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