title: Corneal power evaluation after myopic corneal refractive surgery using artificial neural networks.
abstract: BACKGROUND
  Efficacy and high availability of surgery techniques for refractive defect correction increase the number of patients who undergo to this type of surgery. Regardless of that, with increasing age, more and more patients must undergo cataract surgery. Accurate evaluation of corneal power is an extremely important element affecting the precision of intraocular lens (IOL) power calculation and errors in this procedure could affect quality of life of patients and satisfaction with the service provided. The available device able to measure corneal power have been tested to be not reliable after myopic refractive surgery.
  
  
  METHODS
  Artificial neural networks with error backpropagation and one hidden layer were proposed for corneal power prediction. The article analysed the features acquired from the Pentacam HR tomograph, which was necessary to measure the corneal power. Additionally, several billion iterations of artificial neural networks were conducted for several hundred simulations of different network configurations and different features derived from the Pentacam HR. The analysis was performed on a PC with Intel®Xeon®X5680 3.33 GHz CPU in Matlab®Version 7.11.0.584 (R2010b) with Signal Processing Toolbox Version 7.1 (R2010b), Neural Network Toolbox 7.0 (R2010b) and Statistics Toolbox (R2010b).
  
  
  CONCLUSIONS
  A total corneal power prediction error was obtained for 172 patients (113 patients forming the training set and 59 patients in the test set) with an average age of 32 ± 9.4 years, including 67% of men. The error was at an average level of 0.16 ± 0.14 diopters and its maximum value did not exceed 0.75 dioptres. The Pentacam parameters (measurement results) providing the above result are tangential anterial/posterior. The corneal net power and equivalent k-reading power. The analysis time for a single patient (a single eye) did not exceed 0.1 s, whereas the time of network training was about 3 s for 1000 iterations (the number of neurons in the hidden layer was 400).
authors.affiliation: Department of Biomedical Computer Systems, Faculty of Computer Science and Materials Science, Institute of Computer Science, University of Silesia, ul. Będzińska 39, 41-200, Sosnowiec, Poland. robert.koprowski@us.edu.pl.
authors.forename: Robert
authors.initials: R
authors.lastname: Koprowski
authors.affiliation: Dipartimento Multidisciplinare di Scienze Mediche, Chirurgiche e Odontoiatriche, Seconda Università di Napoli, Naples, Italy.|Centro Grandi Apparecchiature, Seconda Università di Napoli, Naples, Italy.
authors.forename: Michele
authors.initials: M
authors.lastname: Lanza
authors.affiliation: Centro Grandi Apparecchiature, Seconda Università di Napoli, Naples, Italy.
authors.forename: Carlo
authors.initials: C
authors.lastname: Irregolare
mesh_terms: D000328:Adult; D000465:Algorithms; D003315:Cornea / Q000473:pathology / Q000503:physiopathology / Q000601:surgery*; D019781:Corneal Topography; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D009216:Myopia / Q000473:pathology / Q000503:physiopathology / Q000601:surgery*; D016571:Neural Networks, Computer; D011788:Quality of Life; D054140:Refractive Surgical Procedures; D016896:Treatment Outcome; D055815:Young Adult
keywords: Corneal power; IOL power calculation; Neural networks; Refractive surgery; Signal processing