000078718 001__ 78718
000078718 005__ 20200113145616.0
000078718 0247_ $$2doi$$a10.1007/s10237-018-1013-z
000078718 0248_ $$2sideral$$a105440
000078718 037__ $$aART-2018-105440
000078718 041__ $$aeng
000078718 100__ $$aFlecha-Lescún, J.$$uUniversidad de Zaragoza
000078718 245__ $$aTemplate-based methodology for the simulation of intracorneal segment ring implantation in human corneas
000078718 260__ $$c2018
000078718 5060_ $$aAccess copy available to the general public$$fUnrestricted
000078718 5203_ $$aKeratoconus is an idiopathic, non-inflammatory and degenerative corneal disease characterised by a loss of the organisation in the corneal collagen fibrils. As a result, keratoconic corneas present a localised thinning and conical protrusion with irregular astigmatism and high myopia that worsen visual acuity. Intracorneal ring segments (ICRSs) are used in clinic to regularise the corneal surface and to prevent the disease from progressing. Unfortunately, the post-surgical effect of the ICRS is not explicitly accounted beforehand. Traditional treatments rely on population-based nomograms and the experience of the surgeon. In this vein, in silico models could be a clinical aid tool for clinicians to plan the intervention, or to test the post-surgical impact of different clinical scenarios. A semi-automatic computational methodology is presented in order to simulate the ICRS surgical operation and to predict the post-surgical optical outcomes. For the sake of simplicity, circular cross section rings, average corneas and an isotropic hyperelastic material are used. To determine whether the model behaves physiologically and to carry out a sensitivity analysis, a (Formula presented.) full-factorial analysis is carried out. In particular, how the stromal depth insertion, horizontal distance of ring insertion (hDRI) and diameter of the ring’s cross section ((Formula presented.)) are impacting in the spherical and cylindrical power of the cornea is analysed. Afterwards, the kinematics, mechanics and optics of keratoconic corneas after the ICRS insertion are analysed. Based on the parametric study, we can conclude that our model follows clinical trends previously reported. In particular and although there is an improvement in defocus, all corneas presented a change in their optical aberrations. The stromal depth insertion is the parameter that affects the corneal optics the most, whereas hDRI and (Formula presented.) are less important. Not only that, but it is almost impossible to achieve an optimal trade-off between spherical and cylindrical correction. Regarding the mechanical behaviour, inserting the rings at 65% depth or above will cause the cornea to slightly bend. This abnormal stress distribution greatly distorts the corneal optics and, more importantly, could be the cause of clinical problems such as corneal extrusion. Not only that, but our model also supports that rings are acting as restraint elements which relax the stresses of the corneal stroma in the cone of the disease. However, depending on the exact spatial location of the keratoconus, the insertion of rings could promote its evolution instead of preventing it. ICRS inserted deeper will prevent keratoconus in the posterior stroma from growing (relaxation of posterior surface), but will promote its growing if they are located in the anterior surface (increment of stress). In conclusion, the methodology proposed is suitable for simulating long-term mechanical and optical effects of ICRS insertion.
000078718 536__ $$9info:eu-repo/grantAgreement/ES/MINECO/DPI2017-84047-R$$9info:eu-repo/grantAgreement/ES/MINECO/DPI2014-54981-R$$9info:eu-repo/grantAgreement/ES/MINECO/BES-2015-073630$$9info:eu-repo/grantAgreement/ES/DGA/T88
000078718 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/
000078718 590__ $$a2.829$$b2018
000078718 591__ $$aENGINEERING, BIOMEDICAL$$b28 / 80 = 0.35$$c2018$$dQ2$$eT2
000078718 591__ $$aBIOPHYSICS$$b26 / 72 = 0.361$$c2018$$dQ2$$eT2
000078718 592__ $$a1.001$$b2018
000078718 593__ $$aBiotechnology$$c2018$$dQ1
000078718 593__ $$aModeling and Simulation$$c2018$$dQ1
000078718 593__ $$aMechanical Engineering$$c2018$$dQ1
000078718 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion
000078718 700__ $$0(orcid)0000-0001-9713-1813$$aCalvo, B.$$uUniversidad de Zaragoza
000078718 700__ $$aZurita, J.
000078718 700__ $$0(orcid)0000-0002-6773-6667$$aAriza-Gracia, M.Á.
000078718 7102_ $$15004$$2605$$aUniversidad de Zaragoza$$bDpto. Ingeniería Mecánica$$cÁrea Mec.Med.Cont. y Teor.Est.
000078718 773__ $$g17, 4 (2018), 923-93$$pBiomech. model. mechanobiol.$$tBiomechanics and Modeling in Mechanobiology$$x1617-7959
000078718 8564_ $$s724844$$uhttps://zaguan.unizar.es/record/78718/files/texto_completo.pdf$$yPostprint
000078718 8564_ $$s98226$$uhttps://zaguan.unizar.es/record/78718/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint
000078718 909CO $$ooai:zaguan.unizar.es:78718$$particulos$$pdriver
000078718 951__ $$a2020-01-13-14:53:14
000078718 980__ $$aARTICLE