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Strategies for formula constant optimisation for intraocular lens power calculation

BACKGROUND: To investigate modern nonlinear iterative strategies for formula constant optimisation and show the application and results from a large dataset using a set of disclosed theoretical-optical lens power calculation concepts. METHODS: Nonlinear iterative optimisation algorithms were impleme...

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Autores principales: Langenbucher, Achim, Szentmáry, Nóra, Cayless, Alan, Wendelstein, Jascha, Hoffmann, Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9071153/
https://www.ncbi.nlm.nih.gov/pubmed/35511906
http://dx.doi.org/10.1371/journal.pone.0267352
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author Langenbucher, Achim
Szentmáry, Nóra
Cayless, Alan
Wendelstein, Jascha
Hoffmann, Peter
author_facet Langenbucher, Achim
Szentmáry, Nóra
Cayless, Alan
Wendelstein, Jascha
Hoffmann, Peter
author_sort Langenbucher, Achim
collection PubMed
description BACKGROUND: To investigate modern nonlinear iterative strategies for formula constant optimisation and show the application and results from a large dataset using a set of disclosed theoretical-optical lens power calculation concepts. METHODS: Nonlinear iterative optimisation algorithms were implemented for optimising the root mean squared (SoSPE), the mean absolute (SoAPE), the mean (MPE), the standard deviation (SDPE), the median (MEDPE), as well as the 90% confidence interval (CLPE) of the prediction error (PE), defined as the difference between postoperative achieved and formula predicted spherical equivalent power of refraction. Optimisation was performed using the Levenberg-Marquardt algorithm (SoSPE and SoAPE) or the interior point method (MPE, SDPE, MEDPE, CLPE) for the SRKT, Hoffer Q, Holladay 1, Haigis, and Castrop formulae. The results were based on a dataset of measurements made on 888 eyes after implantation of an aspherical hydrophobic monofocal intraocular lens (Vivinex, Hoya). RESULTS: For all formulae and all optimisation metrics, the iterative algorithms showed a fast and stable convergence after a couple of iterations. The results prove that with optimisation for SoSPE, SoAPE, MPE, SDPE, MEDPE, and CLPE the root mean squared PE, mean absolute PE, mean PE, standard deviation of PE, median PE, and confidence interval of PE could be minimised in all situations. The results in terms of cumulative distribution function are quite coherent with optimisation for SoSPE, SoAPE, MPE and MEDPE, whereas with optimisation for SDPE and CLPE the standard deviation and confidence interval of the PE distribution could only be minimised at the cost of a systematic offset in mean and median PE. CONCLUSION: Nonlinear iterative techniques are capable of minimising any statistical metrics (e.g. root mean squared or mean absolute error) of any target parameter (e.g. PE). These optimisation strategies are an important step towards optimising for the target parameters which are used for evaluating the performance of lens power calculation formulae.
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spelling pubmed-90711532022-05-06 Strategies for formula constant optimisation for intraocular lens power calculation Langenbucher, Achim Szentmáry, Nóra Cayless, Alan Wendelstein, Jascha Hoffmann, Peter PLoS One Research Article BACKGROUND: To investigate modern nonlinear iterative strategies for formula constant optimisation and show the application and results from a large dataset using a set of disclosed theoretical-optical lens power calculation concepts. METHODS: Nonlinear iterative optimisation algorithms were implemented for optimising the root mean squared (SoSPE), the mean absolute (SoAPE), the mean (MPE), the standard deviation (SDPE), the median (MEDPE), as well as the 90% confidence interval (CLPE) of the prediction error (PE), defined as the difference between postoperative achieved and formula predicted spherical equivalent power of refraction. Optimisation was performed using the Levenberg-Marquardt algorithm (SoSPE and SoAPE) or the interior point method (MPE, SDPE, MEDPE, CLPE) for the SRKT, Hoffer Q, Holladay 1, Haigis, and Castrop formulae. The results were based on a dataset of measurements made on 888 eyes after implantation of an aspherical hydrophobic monofocal intraocular lens (Vivinex, Hoya). RESULTS: For all formulae and all optimisation metrics, the iterative algorithms showed a fast and stable convergence after a couple of iterations. The results prove that with optimisation for SoSPE, SoAPE, MPE, SDPE, MEDPE, and CLPE the root mean squared PE, mean absolute PE, mean PE, standard deviation of PE, median PE, and confidence interval of PE could be minimised in all situations. The results in terms of cumulative distribution function are quite coherent with optimisation for SoSPE, SoAPE, MPE and MEDPE, whereas with optimisation for SDPE and CLPE the standard deviation and confidence interval of the PE distribution could only be minimised at the cost of a systematic offset in mean and median PE. CONCLUSION: Nonlinear iterative techniques are capable of minimising any statistical metrics (e.g. root mean squared or mean absolute error) of any target parameter (e.g. PE). These optimisation strategies are an important step towards optimising for the target parameters which are used for evaluating the performance of lens power calculation formulae. Public Library of Science 2022-05-05 /pmc/articles/PMC9071153/ /pubmed/35511906 http://dx.doi.org/10.1371/journal.pone.0267352 Text en © 2022 Langenbucher et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Langenbucher, Achim
Szentmáry, Nóra
Cayless, Alan
Wendelstein, Jascha
Hoffmann, Peter
Strategies for formula constant optimisation for intraocular lens power calculation
title Strategies for formula constant optimisation for intraocular lens power calculation
title_full Strategies for formula constant optimisation for intraocular lens power calculation
title_fullStr Strategies for formula constant optimisation for intraocular lens power calculation
title_full_unstemmed Strategies for formula constant optimisation for intraocular lens power calculation
title_short Strategies for formula constant optimisation for intraocular lens power calculation
title_sort strategies for formula constant optimisation for intraocular lens power calculation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9071153/
https://www.ncbi.nlm.nih.gov/pubmed/35511906
http://dx.doi.org/10.1371/journal.pone.0267352
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