[Prediction of myopic shift in paediatric pseudophakia using a neural network: a preliminary study]

Abstract

Objective: To train and evaluate a backpropagation (BP) neural network to predict the pseudophakic refraction of a child at any age.

Methods: The clinical data of paediatric pseudophakia were consecutively collected from the patients for subsequent visits to Cataract Center of Beijing Tongren Hospital during June to October in 2006 and 70 eyes of 41 patients that met the inclusion criteria were identified. We reviewed the case history, preoperative examinations, surgical process and follow-up results of these patients and recorded the main following data: axial length and corneal curvature of both eyes before intraocular lens (IOL) implantation surgery, targeted postoperative refraction, IOL power, laterality, age at cataract extraction and IOL implantation surgery, age and refraction at last follow up. 70 eyes were divided into a training set and a test set by simple random sampling. The training set of 55 eyes was used for training a BP neural network and updating the network weights and biases. The test set of 15 eyes was used to work out the test set prediction of the pseudophakic refraction at last follow up, which was compared with that produced by a logarithmic regression advanced by McClatchey and his colleagues.

Results: For the test data, the correlation between network outputs and target outputs was statistically significant (r = 0.603, P = 0.017); The difference between network outputs and target outputs was not statistically significant (paired-samples t test, P = 0.270). Mean error and mean absolute error from predicted refraction were +0.69 diopters (D) and 1.34 D by BP neural network respectively and were +1.03 D and 1.98 D by logarithmic regression respectively. The differences in predictive errors and absolute errors between two predictive methods were not significant but in absolute errors the P value was close to 0.05 (P = 0.075) by paired-samples t test. The predictions by two predictive methods both underestimated the myopic shift of paediatric pseudophakia and the prediction by logarithmic regression tended towards more hyperopia.

Conclusions: BP neural network improved prediction of pseudophakic refraction of a child at any age compared with the logarithmic regression advanced by McClatchey and his colleagues in this study. It can be a useful tool in predicting myopic shift in paediatric pseudophakia.