Modeling rod and cone photoreceptor cell survival in vivo using optical coherence tomography
Abstract Many retinal diseases involve the loss of light-sensing photoreceptor cells (rods and cones) over time. The severity and distribution of photoreceptor loss varies widely across diseases and affected individuals, so characterizing the degree and pattern of photoreceptor loss can clarify path...
| Main Authors: | , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-04-01
|
| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-33694-y |
| _version_ | 1827956788410974208 |
|---|---|
| author | S. Scott Whitmore Adam P. DeLuca Jeaneen L. Andorf Justine L. Cheng Mahsaw Mansoor Christopher R. Fortenbach D. Brice Critser Jonathan F. Russell Edwin M. Stone Ian C. Han |
| author_facet | S. Scott Whitmore Adam P. DeLuca Jeaneen L. Andorf Justine L. Cheng Mahsaw Mansoor Christopher R. Fortenbach D. Brice Critser Jonathan F. Russell Edwin M. Stone Ian C. Han |
| author_sort | S. Scott Whitmore |
| collection | DOAJ |
| description | Abstract Many retinal diseases involve the loss of light-sensing photoreceptor cells (rods and cones) over time. The severity and distribution of photoreceptor loss varies widely across diseases and affected individuals, so characterizing the degree and pattern of photoreceptor loss can clarify pathophysiology and prognosis. Currently, in vivo visualization of individual photoreceptors requires technology such as adaptive optics, which has numerous limitations and is not widely used. By contrast, optical coherence tomography (OCT) is nearly ubiquitous in daily clinical practice given its ease of image acquisition and detailed visualization of retinal structure. However, OCT cannot resolve individual photoreceptors, and no OCT-based method exists to distinguish between the loss of rods versus cones. Here, we present a computational model that quantitatively estimates rod versus cone photoreceptor loss from OCT. Using histologic data of human photoreceptor topography, we constructed an OCT-based reference model to simulate outer nuclear layer thinning caused by differential loss of rods and cones. The model was able to estimate rod and cone loss using in vivo OCT data from patients with Stargardt disease and healthy controls. Our model provides a powerful new tool to quantify photoreceptor loss using OCT data alone, with potentially broad applications for research and clinical care. |
| first_indexed | 2024-04-09T15:10:11Z |
| format | Article |
| id | doaj.art-ed7c31bc803c43939a09822c391eb221 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-04-09T15:10:11Z |
| publishDate | 2023-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-ed7c31bc803c43939a09822c391eb2212023-04-30T11:17:30ZengNature PortfolioScientific Reports2045-23222023-04-0113111010.1038/s41598-023-33694-yModeling rod and cone photoreceptor cell survival in vivo using optical coherence tomographyS. Scott Whitmore0Adam P. DeLuca1Jeaneen L. Andorf2Justine L. Cheng3Mahsaw Mansoor4Christopher R. Fortenbach5D. Brice Critser6Jonathan F. Russell7Edwin M. Stone8Ian C. Han9The University of Iowa Institute for Vision Research & Department of Ophthalmology and Visual Sciences, Carver College of Medicine, The University of IowaThe University of Iowa Institute for Vision Research & Department of Ophthalmology and Visual Sciences, Carver College of Medicine, The University of IowaThe University of Iowa Institute for Vision Research & Department of Ophthalmology and Visual Sciences, Carver College of Medicine, The University of IowaThe University of Iowa Institute for Vision Research & Department of Ophthalmology and Visual Sciences, Carver College of Medicine, The University of IowaThe University of Iowa Institute for Vision Research & Department of Ophthalmology and Visual Sciences, Carver College of Medicine, The University of IowaThe University of Iowa Institute for Vision Research & Department of Ophthalmology and Visual Sciences, Carver College of Medicine, The University of IowaThe University of Iowa Institute for Vision Research & Department of Ophthalmology and Visual Sciences, Carver College of Medicine, The University of IowaThe University of Iowa Institute for Vision Research & Department of Ophthalmology and Visual Sciences, Carver College of Medicine, The University of IowaThe University of Iowa Institute for Vision Research & Department of Ophthalmology and Visual Sciences, Carver College of Medicine, The University of IowaThe University of Iowa Institute for Vision Research & Department of Ophthalmology and Visual Sciences, Carver College of Medicine, The University of IowaAbstract Many retinal diseases involve the loss of light-sensing photoreceptor cells (rods and cones) over time. The severity and distribution of photoreceptor loss varies widely across diseases and affected individuals, so characterizing the degree and pattern of photoreceptor loss can clarify pathophysiology and prognosis. Currently, in vivo visualization of individual photoreceptors requires technology such as adaptive optics, which has numerous limitations and is not widely used. By contrast, optical coherence tomography (OCT) is nearly ubiquitous in daily clinical practice given its ease of image acquisition and detailed visualization of retinal structure. However, OCT cannot resolve individual photoreceptors, and no OCT-based method exists to distinguish between the loss of rods versus cones. Here, we present a computational model that quantitatively estimates rod versus cone photoreceptor loss from OCT. Using histologic data of human photoreceptor topography, we constructed an OCT-based reference model to simulate outer nuclear layer thinning caused by differential loss of rods and cones. The model was able to estimate rod and cone loss using in vivo OCT data from patients with Stargardt disease and healthy controls. Our model provides a powerful new tool to quantify photoreceptor loss using OCT data alone, with potentially broad applications for research and clinical care.https://doi.org/10.1038/s41598-023-33694-y |
| spellingShingle | S. Scott Whitmore Adam P. DeLuca Jeaneen L. Andorf Justine L. Cheng Mahsaw Mansoor Christopher R. Fortenbach D. Brice Critser Jonathan F. Russell Edwin M. Stone Ian C. Han Modeling rod and cone photoreceptor cell survival in vivo using optical coherence tomography Scientific Reports |
| title | Modeling rod and cone photoreceptor cell survival in vivo using optical coherence tomography |
| title_full | Modeling rod and cone photoreceptor cell survival in vivo using optical coherence tomography |
| title_fullStr | Modeling rod and cone photoreceptor cell survival in vivo using optical coherence tomography |
| title_full_unstemmed | Modeling rod and cone photoreceptor cell survival in vivo using optical coherence tomography |
| title_short | Modeling rod and cone photoreceptor cell survival in vivo using optical coherence tomography |
| title_sort | modeling rod and cone photoreceptor cell survival in vivo using optical coherence tomography |
| url | https://doi.org/10.1038/s41598-023-33694-y |
| work_keys_str_mv | AT sscottwhitmore modelingrodandconephotoreceptorcellsurvivalinvivousingopticalcoherencetomography AT adampdeluca modelingrodandconephotoreceptorcellsurvivalinvivousingopticalcoherencetomography AT jeaneenlandorf modelingrodandconephotoreceptorcellsurvivalinvivousingopticalcoherencetomography AT justinelcheng modelingrodandconephotoreceptorcellsurvivalinvivousingopticalcoherencetomography AT mahsawmansoor modelingrodandconephotoreceptorcellsurvivalinvivousingopticalcoherencetomography AT christopherrfortenbach modelingrodandconephotoreceptorcellsurvivalinvivousingopticalcoherencetomography AT dbricecritser modelingrodandconephotoreceptorcellsurvivalinvivousingopticalcoherencetomography AT jonathanfrussell modelingrodandconephotoreceptorcellsurvivalinvivousingopticalcoherencetomography AT edwinmstone modelingrodandconephotoreceptorcellsurvivalinvivousingopticalcoherencetomography AT ianchan modelingrodandconephotoreceptorcellsurvivalinvivousingopticalcoherencetomography |