Computational Models for Optimization of the Intrastromal Corneal Ring Choice in Patients With Keratoconus Using Corneal Tomography Data
Abstract
PURPOSE:
To evaluate the predictability of asphericity and average keratometry in patients with keratoconus after implantation of intrastromal corneal ring segments (ICRS) using artificial intelligence.
METHODS:
This study included 209 eyes of 160 patients with keratoconus (grades I, II, and III) who had ICRS implanted. The 160 arc length Ferrara ICRS was implanted in all patients. ICRS thickness varied from 150 to 250 μm. Pentacam (Oculus Optikgeräte, Wetzlar, Germany) corneal tomography parameters, clinical data, and ICRS data formed the basis of the 39 studied parameters. Linear regression was used to create the models.
RESULTS:
The best mean absolute error value found was 0.19 for asphericity and was 1.18 for mean keratometry. Comparing the mean absolute error values of the nomogram with the average absolute error of the algorithm, there was an improvement of 0.11 for asphericity and 0.09 for mean keratometry in relation to the current nomogram.
CONCLUSIONS:
The current study showed that the use of computational models could lead to more accurate results and contribute to better surgical decision-making to improve the clinical outcomes in patients with keratoconus implanted with ICRS.
[J Refract Surg. 2018;34(8):547–550.]
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Presented at IEEE-EMBS International Conference on Biomedical and Health Informatics ;June 1–4, 2014 ;Valencia, Spain . Google Scholar
