Pre‐processing visual scenes for retinal prosthesis systems: A comprehensive review
Background Retinal prostheses offer hope for individuals with degenerative retinal diseases by stimulating the remaining retinal cells to partially restore their vision. This review delves into the current advancements in retinal prosthesis technology, with a special emphasis on the pivotal role tha...
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| Veröffentlicht in: | Artificial organs 2024-11, Vol.48 (11), p.1223-1250 |
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| creator | Holiel, Heidi Ahmed Fawzi, Sahar Ali Al‐Atabany, Walid |
| description | Background
Retinal prostheses offer hope for individuals with degenerative retinal diseases by stimulating the remaining retinal cells to partially restore their vision. This review delves into the current advancements in retinal prosthesis technology, with a special emphasis on the pivotal role that image processing and machine learning techniques play in this evolution.
Methods
We provide a comprehensive analysis of the existing implantable devices and optogenetic strategies, delineating their advantages, limitations, and challenges in addressing complex visual tasks. The review extends to various image processing algorithms and deep learning architectures that have been implemented to enhance the functionality of retinal prosthetic devices. We also illustrate the testing results by demonstrating the clinical trials or using Simulated Prosthetic Vision (SPV) through phosphene simulations, which is a critical aspect of simulating visual perception for retinal prosthesis users.
Results
Our review highlights the significant progress in retinal prosthesis technology, particularly its capacity to augment visual perception among the visually impaired. It discusses the integration between image processing and deep learning, illustrating their impact on individual interactions and navigations within the environment through applying clinical trials and also illustrating the limitations of some techniques to be used with current devices, as some approaches only use simulation even on sighted‐normal individuals or rely on qualitative analysis, where some consider realistic perception models and others do not.
Conclusion
This interdisciplinary field holds promise for the future of retinal prostheses, with the potential to significantly enhance the quality of life for individuals with retinal prostheses. Future research directions should pivot towards optimizing phosphene simulations for SPV approaches, considering the distorted and confusing nature of phosphene perception, thereby enriching the visual perception provided by these prosthetic devices. This endeavor will not only improve navigational independence but also facilitate a more immersive interaction with the environment.
This review paper explores the current state of retinal prostheses technology and its potential for restoring vision in individuals with degenerative retinal diseases. It discusses the strengths and limitations of existing implantable devices and optogenetic approaches, and emphasizes the r |
| doi_str_mv | 10.1111/aor.14824 |
| format | Article |
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Retinal prostheses offer hope for individuals with degenerative retinal diseases by stimulating the remaining retinal cells to partially restore their vision. This review delves into the current advancements in retinal prosthesis technology, with a special emphasis on the pivotal role that image processing and machine learning techniques play in this evolution.
Methods
We provide a comprehensive analysis of the existing implantable devices and optogenetic strategies, delineating their advantages, limitations, and challenges in addressing complex visual tasks. The review extends to various image processing algorithms and deep learning architectures that have been implemented to enhance the functionality of retinal prosthetic devices. We also illustrate the testing results by demonstrating the clinical trials or using Simulated Prosthetic Vision (SPV) through phosphene simulations, which is a critical aspect of simulating visual perception for retinal prosthesis users.
Results
Our review highlights the significant progress in retinal prosthesis technology, particularly its capacity to augment visual perception among the visually impaired. It discusses the integration between image processing and deep learning, illustrating their impact on individual interactions and navigations within the environment through applying clinical trials and also illustrating the limitations of some techniques to be used with current devices, as some approaches only use simulation even on sighted‐normal individuals or rely on qualitative analysis, where some consider realistic perception models and others do not.
Conclusion
This interdisciplinary field holds promise for the future of retinal prostheses, with the potential to significantly enhance the quality of life for individuals with retinal prostheses. Future research directions should pivot towards optimizing phosphene simulations for SPV approaches, considering the distorted and confusing nature of phosphene perception, thereby enriching the visual perception provided by these prosthetic devices. This endeavor will not only improve navigational independence but also facilitate a more immersive interaction with the environment.
This review paper explores the current state of retinal prostheses technology and its potential for restoring vision in individuals with degenerative retinal diseases. It discusses the strengths and limitations of existing implantable devices and optogenetic approaches, and emphasizes the role of image‐processing and deep learning techniques in improving visual perception. The paper also highlights the need for future research to enhance the informative quality of phosphene simulations to facilitate safer navigation and increased interaction with the environment.</description><identifier>ISSN: 0160-564X</identifier><identifier>ISSN: 1525-1594</identifier><identifier>EISSN: 1525-1594</identifier><identifier>DOI: 10.1111/aor.14824</identifier><identifier>PMID: 39023279</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Bionic eye ; Clinical trials ; Deep learning ; Devices ; Image enhancement ; Image processing ; Image restoration ; Impact analysis ; Information processing ; Machine learning ; Neural prostheses ; optogenetics ; Perception ; Phosphene ; Prostheses ; Prosthetics ; Qualitative analysis ; Quality of life ; Retina ; Retinal cells ; retinal prosthesis ; Reviews ; saliency‐based detection ; segmentation ; simulated prosthetic vision (SPV) ; Task complexity ; Vision ; Visual aspects ; Visual discrimination learning ; Visual perception ; Visual perception driven algorithms ; Visual stimuli ; Visual tasks</subject><ispartof>Artificial organs, 2024-11, Vol.48 (11), p.1223-1250</ispartof><rights>2024 International Center for Artificial Organ and Transplantation (ICAOT) and Wiley Periodicals LLC.</rights><rights>Copyright © 2024 International Center for Artificial Organs and Transplantation and Wiley Periodicals LLC.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2434-68d7667bcecb8c176c9167e3d325815938e66aee19b27cbbbd00a4f232baf3b53</cites><orcidid>0009-0002-6452-5052</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Faor.14824$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Faor.14824$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,776,780,1411,27903,27904,45553,45554</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/39023279$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Holiel, Heidi Ahmed</creatorcontrib><creatorcontrib>Fawzi, Sahar Ali</creatorcontrib><creatorcontrib>Al‐Atabany, Walid</creatorcontrib><title>Pre‐processing visual scenes for retinal prosthesis systems: A comprehensive review</title><title>Artificial organs</title><addtitle>Artif Organs</addtitle><description>Background
Retinal prostheses offer hope for individuals with degenerative retinal diseases by stimulating the remaining retinal cells to partially restore their vision. This review delves into the current advancements in retinal prosthesis technology, with a special emphasis on the pivotal role that image processing and machine learning techniques play in this evolution.
Methods
We provide a comprehensive analysis of the existing implantable devices and optogenetic strategies, delineating their advantages, limitations, and challenges in addressing complex visual tasks. The review extends to various image processing algorithms and deep learning architectures that have been implemented to enhance the functionality of retinal prosthetic devices. We also illustrate the testing results by demonstrating the clinical trials or using Simulated Prosthetic Vision (SPV) through phosphene simulations, which is a critical aspect of simulating visual perception for retinal prosthesis users.
Results
Our review highlights the significant progress in retinal prosthesis technology, particularly its capacity to augment visual perception among the visually impaired. It discusses the integration between image processing and deep learning, illustrating their impact on individual interactions and navigations within the environment through applying clinical trials and also illustrating the limitations of some techniques to be used with current devices, as some approaches only use simulation even on sighted‐normal individuals or rely on qualitative analysis, where some consider realistic perception models and others do not.
Conclusion
This interdisciplinary field holds promise for the future of retinal prostheses, with the potential to significantly enhance the quality of life for individuals with retinal prostheses. Future research directions should pivot towards optimizing phosphene simulations for SPV approaches, considering the distorted and confusing nature of phosphene perception, thereby enriching the visual perception provided by these prosthetic devices. This endeavor will not only improve navigational independence but also facilitate a more immersive interaction with the environment.
This review paper explores the current state of retinal prostheses technology and its potential for restoring vision in individuals with degenerative retinal diseases. It discusses the strengths and limitations of existing implantable devices and optogenetic approaches, and emphasizes the role of image‐processing and deep learning techniques in improving visual perception. The paper also highlights the need for future research to enhance the informative quality of phosphene simulations to facilitate safer navigation and increased interaction with the environment.</description><subject>Bionic eye</subject><subject>Clinical trials</subject><subject>Deep learning</subject><subject>Devices</subject><subject>Image enhancement</subject><subject>Image processing</subject><subject>Image restoration</subject><subject>Impact analysis</subject><subject>Information processing</subject><subject>Machine learning</subject><subject>Neural prostheses</subject><subject>optogenetics</subject><subject>Perception</subject><subject>Phosphene</subject><subject>Prostheses</subject><subject>Prosthetics</subject><subject>Qualitative analysis</subject><subject>Quality of life</subject><subject>Retina</subject><subject>Retinal cells</subject><subject>retinal prosthesis</subject><subject>Reviews</subject><subject>saliency‐based detection</subject><subject>segmentation</subject><subject>simulated prosthetic vision (SPV)</subject><subject>Task complexity</subject><subject>Vision</subject><subject>Visual aspects</subject><subject>Visual discrimination learning</subject><subject>Visual perception</subject><subject>Visual perception driven algorithms</subject><subject>Visual stimuli</subject><subject>Visual tasks</subject><issn>0160-564X</issn><issn>1525-1594</issn><issn>1525-1594</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><recordid>eNp10MtKw0AUBuBBFFurC19AAm50kTq3TBJ3RbyBoIgFdyEzOdGRXOqcpNKdj-Az-iRObXUhOJsDw8fPOT8h-4yOmX8neevGTCZcbpAhi3gUsiiVm2RImaJhpOTjgOwgvlBKY0nVNhmIlHLB43RIpncOPt8_Zq41gGibp2Busc-rAA00gEHZusBBZxv_5RF2z4AWA1xgBzWeBpPAtPXMwTM0aOfg7dzC2y7ZKvMKYW89R2R6cf5wdhXe3F5en01uQsOlkKFKilipWBswOjEsViZlKgZRCB4l_gaRgFI5AEs1j43WuqA0l6VfXeel0JEYkaNVrl_ttQfsstr6xasqb6DtMRM04YJRyZf08A99aXvnz_KKMcUljRLl1fFKGX8rOiizmbN17hYZo9my68x3nX137e3BOrHXNRS_8qdcD05W4M1WsPg_KZvc3q8ivwDWB4nE</recordid><startdate>202411</startdate><enddate>202411</enddate><creator>Holiel, Heidi Ahmed</creator><creator>Fawzi, Sahar Ali</creator><creator>Al‐Atabany, Walid</creator><general>Wiley Subscription Services, Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><orcidid>https://orcid.org/0009-0002-6452-5052</orcidid></search><sort><creationdate>202411</creationdate><title>Pre‐processing visual scenes for retinal prosthesis systems: A comprehensive review</title><author>Holiel, Heidi Ahmed ; Fawzi, Sahar Ali ; Al‐Atabany, Walid</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2434-68d7667bcecb8c176c9167e3d325815938e66aee19b27cbbbd00a4f232baf3b53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Bionic eye</topic><topic>Clinical trials</topic><topic>Deep learning</topic><topic>Devices</topic><topic>Image enhancement</topic><topic>Image processing</topic><topic>Image restoration</topic><topic>Impact analysis</topic><topic>Information processing</topic><topic>Machine learning</topic><topic>Neural prostheses</topic><topic>optogenetics</topic><topic>Perception</topic><topic>Phosphene</topic><topic>Prostheses</topic><topic>Prosthetics</topic><topic>Qualitative analysis</topic><topic>Quality of life</topic><topic>Retina</topic><topic>Retinal cells</topic><topic>retinal prosthesis</topic><topic>Reviews</topic><topic>saliency‐based detection</topic><topic>segmentation</topic><topic>simulated prosthetic vision (SPV)</topic><topic>Task complexity</topic><topic>Vision</topic><topic>Visual aspects</topic><topic>Visual discrimination learning</topic><topic>Visual perception</topic><topic>Visual perception driven algorithms</topic><topic>Visual stimuli</topic><topic>Visual tasks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Holiel, Heidi Ahmed</creatorcontrib><creatorcontrib>Fawzi, Sahar Ali</creatorcontrib><creatorcontrib>Al‐Atabany, Walid</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Artificial organs</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Holiel, Heidi Ahmed</au><au>Fawzi, Sahar Ali</au><au>Al‐Atabany, Walid</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Pre‐processing visual scenes for retinal prosthesis systems: A comprehensive review</atitle><jtitle>Artificial organs</jtitle><addtitle>Artif Organs</addtitle><date>2024-11</date><risdate>2024</risdate><volume>48</volume><issue>11</issue><spage>1223</spage><epage>1250</epage><pages>1223-1250</pages><issn>0160-564X</issn><issn>1525-1594</issn><eissn>1525-1594</eissn><abstract>Background
Retinal prostheses offer hope for individuals with degenerative retinal diseases by stimulating the remaining retinal cells to partially restore their vision. This review delves into the current advancements in retinal prosthesis technology, with a special emphasis on the pivotal role that image processing and machine learning techniques play in this evolution.
Methods
We provide a comprehensive analysis of the existing implantable devices and optogenetic strategies, delineating their advantages, limitations, and challenges in addressing complex visual tasks. The review extends to various image processing algorithms and deep learning architectures that have been implemented to enhance the functionality of retinal prosthetic devices. We also illustrate the testing results by demonstrating the clinical trials or using Simulated Prosthetic Vision (SPV) through phosphene simulations, which is a critical aspect of simulating visual perception for retinal prosthesis users.
Results
Our review highlights the significant progress in retinal prosthesis technology, particularly its capacity to augment visual perception among the visually impaired. It discusses the integration between image processing and deep learning, illustrating their impact on individual interactions and navigations within the environment through applying clinical trials and also illustrating the limitations of some techniques to be used with current devices, as some approaches only use simulation even on sighted‐normal individuals or rely on qualitative analysis, where some consider realistic perception models and others do not.
Conclusion
This interdisciplinary field holds promise for the future of retinal prostheses, with the potential to significantly enhance the quality of life for individuals with retinal prostheses. Future research directions should pivot towards optimizing phosphene simulations for SPV approaches, considering the distorted and confusing nature of phosphene perception, thereby enriching the visual perception provided by these prosthetic devices. This endeavor will not only improve navigational independence but also facilitate a more immersive interaction with the environment.
This review paper explores the current state of retinal prostheses technology and its potential for restoring vision in individuals with degenerative retinal diseases. It discusses the strengths and limitations of existing implantable devices and optogenetic approaches, and emphasizes the role of image‐processing and deep learning techniques in improving visual perception. The paper also highlights the need for future research to enhance the informative quality of phosphene simulations to facilitate safer navigation and increased interaction with the environment.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>39023279</pmid><doi>10.1111/aor.14824</doi><tpages>28</tpages><orcidid>https://orcid.org/0009-0002-6452-5052</orcidid></addata></record> |
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| source | Wiley Online Library Journals Frontfile Complete |
| subjects | Bionic eye Clinical trials Deep learning Devices Image enhancement Image processing Image restoration Impact analysis Information processing Machine learning Neural prostheses optogenetics Perception Phosphene Prostheses Prosthetics Qualitative analysis Quality of life Retina Retinal cells retinal prosthesis Reviews saliency‐based detection segmentation simulated prosthetic vision (SPV) Task complexity Vision Visual aspects Visual discrimination learning Visual perception Visual perception driven algorithms Visual stimuli Visual tasks |
| title | Pre‐processing visual scenes for retinal prosthesis systems: A comprehensive review |
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