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Foveal Therapy in Blue Cone Monochromacy: Predictions of Visual Potential From Artificial Intelligence

Novel therapeutic approaches for treating inherited retinal degenerations (IRDs) prompt a need to understand which patients with impaired vision have the anatomical potential to gain from participation in a clinical trial. We used supervised machine learning to predict foveal function from foveal st...

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Autores principales: Sumaroka, Alexander, Cideciyan, Artur V., Sheplock, Rebecca, Wu, Vivian, Kohl, Susanne, Wissinger, Bernd, Jacobson, Samuel G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416698/
https://www.ncbi.nlm.nih.gov/pubmed/32848570
http://dx.doi.org/10.3389/fnins.2020.00800
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author Sumaroka, Alexander
Cideciyan, Artur V.
Sheplock, Rebecca
Wu, Vivian
Kohl, Susanne
Wissinger, Bernd
Jacobson, Samuel G.
author_facet Sumaroka, Alexander
Cideciyan, Artur V.
Sheplock, Rebecca
Wu, Vivian
Kohl, Susanne
Wissinger, Bernd
Jacobson, Samuel G.
author_sort Sumaroka, Alexander
collection PubMed
description Novel therapeutic approaches for treating inherited retinal degenerations (IRDs) prompt a need to understand which patients with impaired vision have the anatomical potential to gain from participation in a clinical trial. We used supervised machine learning to predict foveal function from foveal structure in blue cone monochromacy (BCM), an X-linked congenital cone photoreceptor dysfunction secondary to mutations in the OPN1LW/OPN1MW gene cluster. BCM patients with either disease-associated large deletion or missense mutations were studied and results compared with those from subjects with other forms of IRD and various degrees of preserved central structure and function. A machine learning technique was used to associate foveal sensitivities and best-corrected visual acuities to foveal structure in IRD patients. Two random forest (RF) models trained on IRD data were applied to predict foveal function in BCM. A curve fitting method was also used and results compared with those of the RF models. The BCM and IRD patients had a comparable range of foveal structure. IRD patients had peak sensitivity at the fovea. Machine learning could successfully predict foveal sensitivity (FS) results from segmented or un-segmented optical coherence tomography (OCT) input. Application of machine learning predictions to BCM at the fovea showed differences between predicted and measured sensitivities, thereby defining treatment potential. The curve fitting method provided similar results. Given a measure of visual acuity (VA) and foveal outer nuclear layer thickness, the question of how many lines of acuity would represent the best efficacious result for each BCM patient could be answered. We propose that foveal vision improvement potential in BCM is predictable from retinal structure using machine learning and curve fitting approaches. This should allow estimates of maximal efficacy in patients being considered for clinical trials and also guide decisions about dosing.
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spelling pubmed-74166982020-08-25 Foveal Therapy in Blue Cone Monochromacy: Predictions of Visual Potential From Artificial Intelligence Sumaroka, Alexander Cideciyan, Artur V. Sheplock, Rebecca Wu, Vivian Kohl, Susanne Wissinger, Bernd Jacobson, Samuel G. Front Neurosci Neuroscience Novel therapeutic approaches for treating inherited retinal degenerations (IRDs) prompt a need to understand which patients with impaired vision have the anatomical potential to gain from participation in a clinical trial. We used supervised machine learning to predict foveal function from foveal structure in blue cone monochromacy (BCM), an X-linked congenital cone photoreceptor dysfunction secondary to mutations in the OPN1LW/OPN1MW gene cluster. BCM patients with either disease-associated large deletion or missense mutations were studied and results compared with those from subjects with other forms of IRD and various degrees of preserved central structure and function. A machine learning technique was used to associate foveal sensitivities and best-corrected visual acuities to foveal structure in IRD patients. Two random forest (RF) models trained on IRD data were applied to predict foveal function in BCM. A curve fitting method was also used and results compared with those of the RF models. The BCM and IRD patients had a comparable range of foveal structure. IRD patients had peak sensitivity at the fovea. Machine learning could successfully predict foveal sensitivity (FS) results from segmented or un-segmented optical coherence tomography (OCT) input. Application of machine learning predictions to BCM at the fovea showed differences between predicted and measured sensitivities, thereby defining treatment potential. The curve fitting method provided similar results. Given a measure of visual acuity (VA) and foveal outer nuclear layer thickness, the question of how many lines of acuity would represent the best efficacious result for each BCM patient could be answered. We propose that foveal vision improvement potential in BCM is predictable from retinal structure using machine learning and curve fitting approaches. This should allow estimates of maximal efficacy in patients being considered for clinical trials and also guide decisions about dosing. Frontiers Media S.A. 2020-08-03 /pmc/articles/PMC7416698/ /pubmed/32848570 http://dx.doi.org/10.3389/fnins.2020.00800 Text en Copyright © 2020 Sumaroka, Cideciyan, Sheplock, Wu, Kohl, Wissinger and Jacobson. http://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 Neuroscience
Sumaroka, Alexander
Cideciyan, Artur V.
Sheplock, Rebecca
Wu, Vivian
Kohl, Susanne
Wissinger, Bernd
Jacobson, Samuel G.
Foveal Therapy in Blue Cone Monochromacy: Predictions of Visual Potential From Artificial Intelligence
title Foveal Therapy in Blue Cone Monochromacy: Predictions of Visual Potential From Artificial Intelligence
title_full Foveal Therapy in Blue Cone Monochromacy: Predictions of Visual Potential From Artificial Intelligence
title_fullStr Foveal Therapy in Blue Cone Monochromacy: Predictions of Visual Potential From Artificial Intelligence
title_full_unstemmed Foveal Therapy in Blue Cone Monochromacy: Predictions of Visual Potential From Artificial Intelligence
title_short Foveal Therapy in Blue Cone Monochromacy: Predictions of Visual Potential From Artificial Intelligence
title_sort foveal therapy in blue cone monochromacy: predictions of visual potential from artificial intelligence
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7416698/
https://www.ncbi.nlm.nih.gov/pubmed/32848570
http://dx.doi.org/10.3389/fnins.2020.00800
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