title: Accurate intraocular pressure prediction from applanation response data using genetic algorithm and neural networks.
abstract: The fact that Goldmann applanation tonometry does not accurately account for individual corneal elastic stiffness often leads to inaccuracy in the measurement of intraocular pressure (IOP). IOP should account not only for the effect of central corneal thickness (CCT) but should also account for other corneal biomechanical factors. A computational method for accurate and reliable determination of IOP is investigated with a modified applanation tonometer in this paper. The proposed method uses a combined genetic algorithm/neural network procedure to match the clinically measured applanation force-displacement history with that obtained from a nonlinear finite element simulation of applanation. An additional advantage of the proposed method is that it also provides the ability to determine CCT and material properties of the cornea from the same applanation response data. The performance of the proposed method has been demonstrated through a parametric study and via comparison with a well known clinical case. The proposed method is also shown to be computationally efficient, which is an important practical consideration for clinical application.
authors: Department of Civil and Environmental Engineering, University of Illinois at Urban-Champaign, Urbana, IL 61801, USA.
authors: Jamshid
authors: J
authors: Ghaboussi
authors: Tae-Hyun
authors: TH
authors: Kwon
authors: David A
authors: DA
authors: Pecknold
authors: Youssef M A
authors: YM
authors: Hashash
mesh_terms: D000293:Adolescent; D000465:Algorithms; D003198:Computer Simulation; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005260:Female; D006801:Humans; D007429:Intraocular Pressure / Q000502:physiology*; D008365:Manometry / Q000379:methods*; D008954:Models, Biological; D008957:Models, Genetic; D009415:Nerve Net; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity