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Inferred retinal sensitivity in recessive Stargardt disease using machine learning

Spatially-resolved retinal function can be measured by psychophysical testing like fundus-controlled perimetry (FCP or ‘microperimetry’). It may serve as a performance outcome measure in emerging interventional clinical trials for macular diseases as requested by regulatory agencies. As FCP constitu...

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Autores principales: Müller, Philipp L., Odainic, Alexandru, Treis, Tim, Herrmann, Philipp, Tufail, Adnan, Holz, Frank G., Pfau, Maximilian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809282/
https://www.ncbi.nlm.nih.gov/pubmed/33446864
http://dx.doi.org/10.1038/s41598-020-80766-4
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author Müller, Philipp L.
Odainic, Alexandru
Treis, Tim
Herrmann, Philipp
Tufail, Adnan
Holz, Frank G.
Pfau, Maximilian
author_facet Müller, Philipp L.
Odainic, Alexandru
Treis, Tim
Herrmann, Philipp
Tufail, Adnan
Holz, Frank G.
Pfau, Maximilian
author_sort Müller, Philipp L.
collection PubMed
description Spatially-resolved retinal function can be measured by psychophysical testing like fundus-controlled perimetry (FCP or ‘microperimetry’). It may serve as a performance outcome measure in emerging interventional clinical trials for macular diseases as requested by regulatory agencies. As FCP constitute laborious examinations, we have evaluated a machine-learning-based approach to predict spatially-resolved retinal function (’inferred sensitivity’) based on microstructural imaging (obtained by spectral domain optical coherence tomography) and patient data in recessive Stargardt disease. Using nested cross-validation, prediction accuracies of (mean absolute error, MAE [95% CI]) 4.74 dB [4.48–4.99] were achieved. After additional inclusion of limited FCP data, the latter reached 3.89 dB [3.67–4.10] comparable to the test–retest MAE estimate of 3.51 dB [3.11–3.91]. Analysis of the permutation importance revealed, that the IS&OS and RPE thickness were the most important features for the prediction of retinal sensitivity. ’Inferred sensitivity’, herein, enables to accurately estimate differential effects of retinal microstructure on spatially-resolved function in Stargardt disease, and might be used as quasi-functional surrogate marker for a refined and time-efficient investigation of possible functionally relevant treatment effects or disease progression.
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spelling pubmed-78092822021-01-15 Inferred retinal sensitivity in recessive Stargardt disease using machine learning Müller, Philipp L. Odainic, Alexandru Treis, Tim Herrmann, Philipp Tufail, Adnan Holz, Frank G. Pfau, Maximilian Sci Rep Article Spatially-resolved retinal function can be measured by psychophysical testing like fundus-controlled perimetry (FCP or ‘microperimetry’). It may serve as a performance outcome measure in emerging interventional clinical trials for macular diseases as requested by regulatory agencies. As FCP constitute laborious examinations, we have evaluated a machine-learning-based approach to predict spatially-resolved retinal function (’inferred sensitivity’) based on microstructural imaging (obtained by spectral domain optical coherence tomography) and patient data in recessive Stargardt disease. Using nested cross-validation, prediction accuracies of (mean absolute error, MAE [95% CI]) 4.74 dB [4.48–4.99] were achieved. After additional inclusion of limited FCP data, the latter reached 3.89 dB [3.67–4.10] comparable to the test–retest MAE estimate of 3.51 dB [3.11–3.91]. Analysis of the permutation importance revealed, that the IS&OS and RPE thickness were the most important features for the prediction of retinal sensitivity. ’Inferred sensitivity’, herein, enables to accurately estimate differential effects of retinal microstructure on spatially-resolved function in Stargardt disease, and might be used as quasi-functional surrogate marker for a refined and time-efficient investigation of possible functionally relevant treatment effects or disease progression. Nature Publishing Group UK 2021-01-14 /pmc/articles/PMC7809282/ /pubmed/33446864 http://dx.doi.org/10.1038/s41598-020-80766-4 Text en © The Author(s) 2021 Open Access This 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/.
spellingShingle Article
Müller, Philipp L.
Odainic, Alexandru
Treis, Tim
Herrmann, Philipp
Tufail, Adnan
Holz, Frank G.
Pfau, Maximilian
Inferred retinal sensitivity in recessive Stargardt disease using machine learning
title Inferred retinal sensitivity in recessive Stargardt disease using machine learning
title_full Inferred retinal sensitivity in recessive Stargardt disease using machine learning
title_fullStr Inferred retinal sensitivity in recessive Stargardt disease using machine learning
title_full_unstemmed Inferred retinal sensitivity in recessive Stargardt disease using machine learning
title_short Inferred retinal sensitivity in recessive Stargardt disease using machine learning
title_sort inferred retinal sensitivity in recessive stargardt disease using machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7809282/
https://www.ncbi.nlm.nih.gov/pubmed/33446864
http://dx.doi.org/10.1038/s41598-020-80766-4
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