Inferred retinal sensitivity in recessive Stargardt disease using machine learning

Abstract 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...

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Autores principales:	Philipp L. Müller, Alexandru Odainic, Tim Treis, Philipp Herrmann, Adnan Tufail, Frank G. Holz, Maximilian Pfau
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8c11343c1f63442ba53d909e99b0358f
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spelling	oai:doaj.org-article:8c11343c1f63442ba53d909e99b0358f2021-12-02T14:01:21ZInferred retinal sensitivity in recessive Stargardt disease using machine learning10.1038/s41598-020-80766-42045-2322https://doaj.org/article/8c11343c1f63442ba53d909e99b0358f2021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80766-4https://doaj.org/toc/2045-2322Abstract 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.Philipp L. MüllerAlexandru OdainicTim TreisPhilipp HerrmannAdnan TufailFrank G. HolzMaximilian PfauNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Philipp L. Müller Alexandru Odainic Tim Treis Philipp Herrmann Adnan Tufail Frank G. Holz Maximilian Pfau Inferred retinal sensitivity in recessive Stargardt disease using machine learning
description	Abstract 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.
format	article
author	Philipp L. Müller Alexandru Odainic Tim Treis Philipp Herrmann Adnan Tufail Frank G. Holz Maximilian Pfau
author_facet	Philipp L. Müller Alexandru Odainic Tim Treis Philipp Herrmann Adnan Tufail Frank G. Holz Maximilian Pfau
author_sort	Philipp L. Müller
title	Inferred retinal sensitivity in recessive Stargardt disease using machine learning
title_short	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_sort	inferred retinal sensitivity in recessive stargardt disease using machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/8c11343c1f63442ba53d909e99b0358f
work_keys_str_mv	AT philipplmuller inferredretinalsensitivityinrecessivestargardtdiseaseusingmachinelearning AT alexandruodainic inferredretinalsensitivityinrecessivestargardtdiseaseusingmachinelearning AT timtreis inferredretinalsensitivityinrecessivestargardtdiseaseusingmachinelearning AT philippherrmann inferredretinalsensitivityinrecessivestargardtdiseaseusingmachinelearning AT adnantufail inferredretinalsensitivityinrecessivestargardtdiseaseusingmachinelearning AT frankgholz inferredretinalsensitivityinrecessivestargardtdiseaseusingmachinelearning AT maximilianpfau inferredretinalsensitivityinrecessivestargardtdiseaseusingmachinelearning
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Inferred retinal sensitivity in recessive Stargardt disease using machine learning

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