Abstract
BackgroundTo analyze the clinical results of an artificial neural network (ANN) that has been processed in order to improve the predictability of intracorneal ring segments (ICRS) implantation in keratoconus.MethodsThis retrospective, comparative, nonrandomized, pilot, clinical study included a cohort of 20 keratoconic eyes implanted with intracorneal ring segments KeraRing (Mediphacos, Belo Horizonte, Brazil) using the ANN (ANN group) and 20 keratoconic eyes implanted with KeraRing using the manufacturer’s nomograms (nomogram group). Uncorrected distance visual acuity (UDVA), corrected distance visual acuity (CDVA) (visual acuity is expressed in decimal value and in LogMAR value in brackets), manifest refraction, corneal topography, tomography, aberrometry, pachymetry and volume analysis (Sirius System. CSO, Firenze, Italy) were performed during the preoperative visit; and the two groups, ANN group and nomogram group, did not differ significantly preoperatively in all of the parameters evaluated. These preoperative values were compared with the results obtained at the third-month visit. Mann-Whitney test and Wilcoxon test were used for the statistical analyses.ResultsThe spherical equivalent and the keratometric values decreased significantly in both groups. The CDVA improved from 0.60 ± 0.23 (0.22 LogMAR) pre-operatively to 0.73 ± 0.21 (0.14 LogMAR) post-operatively in the ANN group (p < 0.005), and from 0.54 ± 0.19 (0.27 LogMAR) pre-operatively to 0.62 ± 0.19 (0.21 LogMAR) post-operatively in the nomogram group (p < 0.01), with statistically significant difference between the two groups (p < 0.05), being better in the ANN group. Coma-like aberrations decreased significantly in the ANN group, while in the nomogram group they did not change significantly, but no statistically significant difference was found between the two groups.ConclusionsANN to guide ICRS provides an increase in the visual acuity, reduction in the spherical equivalent and improvement in the optical quality of keratoconus patients. ANN gives better results when compared with the manufacturer’s nomograms in terms of better corrected vision and reduction of the coma-like aberrations. The constant inclusion of new cases will make the predictability of ANN increasingly better as the software finetunes its learning.
Highlights
To analyze the clinical results of an artificial neural network (ANN) that has been processed in order to improve the predictability of intracorneal ring segments (ICRS) implantation in keratoconus
The current study comprises a cohort of 20 consecutive keratoconic eyes implanted with intracorneal ring segments using the ANN (18 patients; 14 males and 4 females; mean age of 29.6 ± 11.1)
Eight eyes of the ANN group received only intracorneal rings and 12 eyes underwent intracorneal rings associated with Collagen cross-linking (CXL)
Summary
To analyze the clinical results of an artificial neural network (ANN) that has been processed in order to improve the predictability of intracorneal ring segments (ICRS) implantation in keratoconus. For the management of keratoconus, different therapeutic options are available, such as rigid gaspermeable contact lenses, corneal collagen cross-linking (CXL), intracorneal ring segments (ICRS) implantation and keratoplasty. ICRS represents an additive surgical procedure, which improves visual outcome and contact lens tolerance, re-shaping highly distorted corneal surfaces [4] and redistributing the asymmetrical corneal stress caused by the biomechanical decompensation [5]. They have shown safety, reversibility and stability [6, 7], and can delay, and sometimes avoid, corneal grafting in keratoconus patients [8]. The characteristics of ICRS to be implanted, including number, arc length and thickness, are chosen in the majority of cases according to the manufacturer’s nomogram, which most of the time is based on data with poor predictability in keratoconus cases such as refraction and astigmatism, and are empirical, and sometimes based on the experience of the surgeon, which can be subjective
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
