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Artificial neural network to guide intracorneal ring segments implantation for keratoconus treatment: a pilot study

BACKGROUND: 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. METHODS: This retrospective, comparative, nonrandomized, pilot, clinical study included a...

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Autores principales: Fariselli, Chiara, Vega-Estrada, Alfredo, Arnalich-Montiel, Francisco, Alio, Jorge L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7144046/
https://www.ncbi.nlm.nih.gov/pubmed/32292796
http://dx.doi.org/10.1186/s40662-020-00184-5
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author Fariselli, Chiara
Vega-Estrada, Alfredo
Arnalich-Montiel, Francisco
Alio, Jorge L.
author_facet Fariselli, Chiara
Vega-Estrada, Alfredo
Arnalich-Montiel, Francisco
Alio, Jorge L.
author_sort Fariselli, Chiara
collection PubMed
description BACKGROUND: 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. METHODS: This 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. RESULTS: The 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. CONCLUSIONS: ANN 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.
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spelling pubmed-71440462020-04-14 Artificial neural network to guide intracorneal ring segments implantation for keratoconus treatment: a pilot study Fariselli, Chiara Vega-Estrada, Alfredo Arnalich-Montiel, Francisco Alio, Jorge L. Eye Vis (Lond) Research BACKGROUND: 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. METHODS: This 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. RESULTS: The 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. CONCLUSIONS: ANN 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. BioMed Central 2020-04-09 /pmc/articles/PMC7144046/ /pubmed/32292796 http://dx.doi.org/10.1186/s40662-020-00184-5 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Fariselli, Chiara
Vega-Estrada, Alfredo
Arnalich-Montiel, Francisco
Alio, Jorge L.
Artificial neural network to guide intracorneal ring segments implantation for keratoconus treatment: a pilot study
title Artificial neural network to guide intracorneal ring segments implantation for keratoconus treatment: a pilot study
title_full Artificial neural network to guide intracorneal ring segments implantation for keratoconus treatment: a pilot study
title_fullStr Artificial neural network to guide intracorneal ring segments implantation for keratoconus treatment: a pilot study
title_full_unstemmed Artificial neural network to guide intracorneal ring segments implantation for keratoconus treatment: a pilot study
title_short Artificial neural network to guide intracorneal ring segments implantation for keratoconus treatment: a pilot study
title_sort artificial neural network to guide intracorneal ring segments implantation for keratoconus treatment: a pilot study
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7144046/
https://www.ncbi.nlm.nih.gov/pubmed/32292796
http://dx.doi.org/10.1186/s40662-020-00184-5
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