Accurate intraocular pressure prediction from applanation response data using genetic algorithm and neural networks

Journal of Biomechanics

Volumn 42, Issue 14, 2009, Pages 2301-2306

  Ghaboussi, Jamshid ^a   Kwon, Tae Hyun ^b   Pecknold, David A ^a   Hashash, Youssef M A ^a  

^a UNIVERSITY OF ILLINOIS AT URBANA CHAMPAIGN   (United States)

^b ESI   (United States)

Author keywords

Central corneal thickness; Cornea; Finite element simulation; Genetic algorithm; Goldmann applanation tonometry; Intraocular pressure; Neural networks

Indexed keywords

CENTRAL CORNEAL THICKNESS; CORNEA; FINITE ELEMENT SIMULATION; GOLDMANN APPLANATION TONOMETRY; INTRAOCULAR PRESSURE;

BIOMECHANICS; COMPUTATIONAL EFFICIENCY; FINITE ELEMENT METHOD; GENETIC ALGORITHMS; NEURAL NETWORKS; OPTICAL VARIABLES MEASUREMENT;

COMPUTER SIMULATION;

ACCURACY; ADOLESCENT; ARTICLE; ARTIFICIAL NEURAL NETWORK; CANNULATION; CASE REPORT; COMPUTER SIMULATION; CONTROLLED STUDY; CORNEA THICKNESS; FEMALE; FINITE ELEMENT ANALYSIS; GENETIC ALGORITHM; HUMAN; INTRAOCULAR HYPERTENSION; INTRAOCULAR PRESSURE; PARAMETRIC TEST; PREDICTION; PRIORITY JOURNAL; RELIABILITY; TONOMETER;

ADOLESCENT; ALGORITHMS; COMPUTER SIMULATION; DIAGNOSIS, COMPUTER-ASSISTED; FEMALE; HUMANS; INTRAOCULAR PRESSURE; MANOMETRY; MODELS, BIOLOGICAL; MODELS, GENETIC; NERVE NET; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 70349566395     PISSN: 00219290     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jbiomech.2009.06.020     Document Type: Article

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