Using artificial intelligence in the interpretation of corneal topography for laser vision correction

Document Type : Original Articles

Authors

Benha University

Abstract

PURPOSE: Development of an artificial intelligence program for interpretation corneal tomography.
SETTING: Ebsar eye center, Benha, Qalyopia, Egypt.
METHODS: In this retrospective cohort study, we analyzed the tomography of 611 eyes of 4 groups of patients using manual interpretation and Hamed’s Laser Vision Correction Interpreter as well.
RESULTS: There is a statistically significant difference between group 2 and group 1 regarding the inter eye differences in thinnest location (P-value 0.021) and also manifest refraction spherical equivalent (P-value 0.011). The mean of both was significantly high in group 1 (patients with postoperative ectasia) 17.0 ± 7.87 and -5.56 ± 2.16 respectively. There is a statistically significant difference between group 3 and group 1 regarding percent tissue altered (P-value <0.001) and residual stromal thickness (P-value <0.001). The mean of percent tissue altered was significantly higher among patients who had post-laser kerato-refractive surgery ectasia group (37.23 ± 5.18) while the mean of residual stromal thickness was significantly low among this group (328.25 ± 41.6). In respect to group 4, the mean of the Inter eye score was 3.38 ± 1.04, and the mean of relative thickness map was -9.2 ± 0.596. The shape of the thickness profile map curve was a quick slope in 61.5% of eyes and normal in 38.5% of eyes in group 4. Some ectasia risk factors were missed during manual interpretation of topography that led to post LVC ectasia.
CONCLUSIONS: Developing an artificial intelligence system that can interpret corneal tomography will alleviate the human errors of manual interpretation.

Keywords

Egyptian Journal of Ophthalmology, (Mansoura Ophthalmic Center)
Volume 1, Issue 4
December 2021
Pages 176-188

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