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A Potential Screening Index of Corneal Biomechanics in Healthy Subjects, Forme Fruste Keratoconus Patients and Clinical Keratoconus Patients

Purpose: This study aims to evaluate the validity of corneal elastic modulus (E) calculated from corneal visualization Scheimpflug technology (Corvis ST) in diagnosing keratoconus (KC) and forme fruste keratoconus (FFKC). Methods: Fifty KC patients (50 eyes), 36 FFKC patients (36 eyes, the eyes were...

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Autores principales: Tian, Lei, Qin, Xiao, Zhang, Hui, Zhang, Di, Guo, Li-Li, Zhang, Hai-Xia, Wu, Ying, Jie, Ying, Li, Lin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733640/
https://www.ncbi.nlm.nih.gov/pubmed/35004638
http://dx.doi.org/10.3389/fbioe.2021.766605
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author Tian, Lei
Qin, Xiao
Zhang, Hui
Zhang, Di
Guo, Li-Li
Zhang, Hai-Xia
Wu, Ying
Jie, Ying
Li, Lin
author_facet Tian, Lei
Qin, Xiao
Zhang, Hui
Zhang, Di
Guo, Li-Li
Zhang, Hai-Xia
Wu, Ying
Jie, Ying
Li, Lin
author_sort Tian, Lei
collection PubMed
description Purpose: This study aims to evaluate the validity of corneal elastic modulus (E) calculated from corneal visualization Scheimpflug technology (Corvis ST) in diagnosing keratoconus (KC) and forme fruste keratoconus (FFKC). Methods: Fifty KC patients (50 eyes), 36 FFKC patients (36 eyes, the eyes were without morphological abnormality, while the contralateral eye was diagnosed as clinical keratoconus), and 50 healthy patients (50 eyes) were enrolled and underwent Corvis measurements. We calculated E according to the relation between airpuff force and corneal apical displacement. One-way analysis of variance (ANOVA) and receiver operating characteristic (ROC) curve analysis were used to identify the predictive accuracy of the E and other dynamic corneal response (DCR) parameters. Besides, we used backpropagation (BP) neural network to establish the keratoconus diagnosis model. Results: 1) There was significant difference between KC and healthy subjects in the following DCR parameters: the first/second applanation time (A1T/A2T), velocity at first/second applanation (A1V/A2V), the highest concavity time (HCT), peak distance (PD), deformation amplitude (DA), Ambrosio relational thickness to the horizontal profile (ARTh). 2) A1T and E were smaller in FFKC and KC compared with healthy subjects. 3) ROC analysis showed that E (AUC = 0.746) was more accurate than other DCR parameters in detecting FFKC (AUC of these DCR parameters was not more than 0.719). 4) Keratoconus diagnosis model by BP neural network showed a more accurate diagnostic efficiency of 92.5%. The ROC analysis showed that the predicted value (AUC = 0.877) of BP neural network model was more sensitive in the detection FFKC than the Corvis built-in parameters CBI (AUC = 0.610, p = 0.041) and TBI (AUC = 0.659, p = 0.034). Conclusion: Corneal elastic modulus was found to have improved predictability in detecting FFKC patients from healthy subjects and may be used as an additional parameter for the diagnosis of keratoconus.
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spelling pubmed-87336402022-01-07 A Potential Screening Index of Corneal Biomechanics in Healthy Subjects, Forme Fruste Keratoconus Patients and Clinical Keratoconus Patients Tian, Lei Qin, Xiao Zhang, Hui Zhang, Di Guo, Li-Li Zhang, Hai-Xia Wu, Ying Jie, Ying Li, Lin Front Bioeng Biotechnol Bioengineering and Biotechnology Purpose: This study aims to evaluate the validity of corneal elastic modulus (E) calculated from corneal visualization Scheimpflug technology (Corvis ST) in diagnosing keratoconus (KC) and forme fruste keratoconus (FFKC). Methods: Fifty KC patients (50 eyes), 36 FFKC patients (36 eyes, the eyes were without morphological abnormality, while the contralateral eye was diagnosed as clinical keratoconus), and 50 healthy patients (50 eyes) were enrolled and underwent Corvis measurements. We calculated E according to the relation between airpuff force and corneal apical displacement. One-way analysis of variance (ANOVA) and receiver operating characteristic (ROC) curve analysis were used to identify the predictive accuracy of the E and other dynamic corneal response (DCR) parameters. Besides, we used backpropagation (BP) neural network to establish the keratoconus diagnosis model. Results: 1) There was significant difference between KC and healthy subjects in the following DCR parameters: the first/second applanation time (A1T/A2T), velocity at first/second applanation (A1V/A2V), the highest concavity time (HCT), peak distance (PD), deformation amplitude (DA), Ambrosio relational thickness to the horizontal profile (ARTh). 2) A1T and E were smaller in FFKC and KC compared with healthy subjects. 3) ROC analysis showed that E (AUC = 0.746) was more accurate than other DCR parameters in detecting FFKC (AUC of these DCR parameters was not more than 0.719). 4) Keratoconus diagnosis model by BP neural network showed a more accurate diagnostic efficiency of 92.5%. The ROC analysis showed that the predicted value (AUC = 0.877) of BP neural network model was more sensitive in the detection FFKC than the Corvis built-in parameters CBI (AUC = 0.610, p = 0.041) and TBI (AUC = 0.659, p = 0.034). Conclusion: Corneal elastic modulus was found to have improved predictability in detecting FFKC patients from healthy subjects and may be used as an additional parameter for the diagnosis of keratoconus. Frontiers Media S.A. 2021-12-23 /pmc/articles/PMC8733640/ /pubmed/35004638 http://dx.doi.org/10.3389/fbioe.2021.766605 Text en Copyright © 2021 Tian, Qin, Zhang, Zhang, Guo, Zhang, Wu, Jie and Li. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Bioengineering and Biotechnology
Tian, Lei
Qin, Xiao
Zhang, Hui
Zhang, Di
Guo, Li-Li
Zhang, Hai-Xia
Wu, Ying
Jie, Ying
Li, Lin
A Potential Screening Index of Corneal Biomechanics in Healthy Subjects, Forme Fruste Keratoconus Patients and Clinical Keratoconus Patients
title A Potential Screening Index of Corneal Biomechanics in Healthy Subjects, Forme Fruste Keratoconus Patients and Clinical Keratoconus Patients
title_full A Potential Screening Index of Corneal Biomechanics in Healthy Subjects, Forme Fruste Keratoconus Patients and Clinical Keratoconus Patients
title_fullStr A Potential Screening Index of Corneal Biomechanics in Healthy Subjects, Forme Fruste Keratoconus Patients and Clinical Keratoconus Patients
title_full_unstemmed A Potential Screening Index of Corneal Biomechanics in Healthy Subjects, Forme Fruste Keratoconus Patients and Clinical Keratoconus Patients
title_short A Potential Screening Index of Corneal Biomechanics in Healthy Subjects, Forme Fruste Keratoconus Patients and Clinical Keratoconus Patients
title_sort potential screening index of corneal biomechanics in healthy subjects, forme fruste keratoconus patients and clinical keratoconus patients
topic Bioengineering and Biotechnology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8733640/
https://www.ncbi.nlm.nih.gov/pubmed/35004638
http://dx.doi.org/10.3389/fbioe.2021.766605
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