Official ESCRS | European Society of Cataract & Refractive Surgeons
Vienna 2018 Delegate Registration Programme Exhibition Virtual Exhibition Satellites 2018 Survey


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Forecasting the refractive error post cataract surgery using Tensorflow and Tensorboard, two open source machine learning tools developed by Google brains

Poster Details

First Author: O.Richoz SWITZERLAND

Co Author(s):    D. Tabibian   P. Aleksandra   A. Konstantinos   M. El Wardani   K. Hashemi   G. Kymionis     

Abstract Details


To improve the refractive accuracy post cataract surgery using the SRK-T calculation and a Deep Neural Network Classifier using the Tensorflow library.


Jules Gonin Eye hospital University of Lausanne


122 eyes had an uncomplicated cataract surgery at the Jules Gonin Eye hospital all received the same IOL brand (AMO Technis ZCB00), all the capsulorhexis size were between 4.5-6.0 mm of diameter. A deep neuronal network was trained using 2/3 of the eyes as a training set and 1/3 of the eyes as a testing set. 11 different variables feed the neuronal network (age ,AL ,K1 ,K2…) and 3 different results were expected representing the mismatch between the 6 weeks post refractive values and SRK-T calculated results (between -0.25 +0.25, more than -0.25 and more than +0.25.


By using 50’000 learning steps with a three hidden layer unit of respectively 100, 50 and 25 nodes, the deep neuronal network shows an accuracy of up to 77.8%.


Deep Neural Network technology freely accessible with Tensorflow offers a great opportunity for improving the refractive outcome of cataract surgery. More data are needed for improving the accuracy of the algorithm.

Financial Disclosure:


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