Pub Date : 2025-04-24DOI: 10.1016/j.preteyeres.2025.101361
Sucheta Bhattacharya , Jugasmita Deka , Thomas Avallone , Levi Todd
Retinal neurodegeneration leads to irreversible blindness due to the mammalian nervous system's inability to regenerate lost neurons. Efforts to regenerate retina involve two main strategies: stimulating endogenous cells to reprogram into neurons or transplanting stem-cell derived neurons into the degenerated retina. However, both approaches must overcome a major barrier in getting new neurons to grow back down the optic nerve and connect to appropriate visual targets in environments that differ significantly from developmental conditions. While immune privilege has historically been associated with the central nervous system, an emerging literature highlights the active role of immune cells in shaping neurodegeneration and regeneration. This review explores the neuroimmune interface in retinal repair, dissecting how immune interactions influence glial reprogramming, transplantation outcomes, and axonal regeneration. By integrating insights from regenerative species with mammalian models, we highlight novel immunomodulatory strategies to optimize retinal regeneration.
{"title":"The neuroimmune interface in retinal regeneration","authors":"Sucheta Bhattacharya , Jugasmita Deka , Thomas Avallone , Levi Todd","doi":"10.1016/j.preteyeres.2025.101361","DOIUrl":"10.1016/j.preteyeres.2025.101361","url":null,"abstract":"<div><div>Retinal neurodegeneration leads to irreversible blindness due to the mammalian nervous system's inability to regenerate lost neurons. Efforts to regenerate retina involve two main strategies: stimulating endogenous cells to reprogram into neurons or transplanting stem-cell derived neurons into the degenerated retina. However, both approaches must overcome a major barrier in getting new neurons to grow back down the optic nerve and connect to appropriate visual targets in environments that differ significantly from developmental conditions. While immune privilege has historically been associated with the central nervous system, an emerging literature highlights the active role of immune cells in shaping neurodegeneration and regeneration. This review explores the neuroimmune interface in retinal repair, dissecting how immune interactions influence glial reprogramming, transplantation outcomes, and axonal regeneration. By integrating insights from regenerative species with mammalian models, we highlight novel immunomodulatory strategies to optimize retinal regeneration.</div></div>","PeriodicalId":21159,"journal":{"name":"Progress in Retinal and Eye Research","volume":"106 ","pages":"Article 101361"},"PeriodicalIF":18.6,"publicationDate":"2025-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877388","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-22DOI: 10.1016/j.preteyeres.2025.101359
Lola P. Lozano , Renato Jensen , Madeleine Jennisch , Narendra G. Pandala , Farzad Jamshidi , H. Culver Boldt , Budd A. Tucker , Elaine M. Binkley
In this review, we aim to provide a survey of hereditable tumor predisposition syndromes with a Mendelian inheritance pattern and ocular involvement. We focus our discussion on von Hippel-Lindau disease, neurofibromatosis type 1, NF2-related schwannomatosis, tuberous sclerosis complex, retinoblastoma, and the BAP1 tumor predisposition syndrome. For each of the six diseases, we discuss the clinical presentation and the molecular pathophysiology. We emphasize the genetics, current research models, and therapeutic developments. After reading each disease section, readers should possess an understanding of the clinical presentation, genetic causes and inheritance patterns, and current state of research in disease modeling and treatment.
{"title":"Genetics and current research models of Mendelian tumor predisposition syndromes with ocular involvement","authors":"Lola P. Lozano , Renato Jensen , Madeleine Jennisch , Narendra G. Pandala , Farzad Jamshidi , H. Culver Boldt , Budd A. Tucker , Elaine M. Binkley","doi":"10.1016/j.preteyeres.2025.101359","DOIUrl":"10.1016/j.preteyeres.2025.101359","url":null,"abstract":"<div><div>In this review, we aim to provide a survey of hereditable tumor predisposition syndromes with a Mendelian inheritance pattern and ocular involvement. We focus our discussion on von Hippel-Lindau disease, neurofibromatosis type 1, NF2-related schwannomatosis, tuberous sclerosis complex, retinoblastoma, and the BAP1 tumor predisposition syndrome. For each of the six diseases, we discuss the clinical presentation and the molecular pathophysiology. We emphasize the genetics, current research models, and therapeutic developments. After reading each disease section, readers should possess an understanding of the clinical presentation, genetic causes and inheritance patterns, and current state of research in disease modeling and treatment.</div></div>","PeriodicalId":21159,"journal":{"name":"Progress in Retinal and Eye Research","volume":"106 ","pages":"Article 101359"},"PeriodicalIF":18.6,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143877389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-18DOI: 10.1016/j.preteyeres.2025.101357
Alessandro Arrigo , Ottavio Cremona , Emanuela Aragona , Filippo Casoni , Giacomo Consalez , Rüya Merve Dogru , Stefanie M. Hauck , Alessio Antropoli , Lorenzo Bianco , Maurizio Battaglia Parodi , Francesco Bandello , Antje Grosche
Müller cells are a crucial retinal cell type involved in multiple regulatory processes and functions that are essential for retinal health and functionality. Acting as structural and functional support for retinal neurons and photoreceptors, Müller cells produce growth factors, regulate ion and fluid homeostasis, and facilitate neuronal signaling. They play a pivotal role in retinal morphogenesis and cell differentiation, significantly contributing to macular development.
Due to their radial morphology and unique cytoskeletal organization, Müller cells act as optical fibers, efficiently channeling photons directly to the photoreceptors. In response to retinal damage, Müller cells undergo specific gene expression and functional changes that serve as a first line of defense for neurons, but can also lead to unwarranted cell dysfunction, contributing to cell death and neurodegeneration. In some species, Müller cells can reactivate their developmental program, promoting retinal regeneration and plasticity—a remarkable ability that holds promising therapeutic potential if harnessed in mammals.
The crucial and multifaceted roles of Müller cells—that we propose to collectively call “Müller cells trophism"—highlight the necessity of maintaining their functionality. Dysfunction of Müller cells, termed “Müller cells pathology,” has been associated with a plethora of retinal diseases, including age-related macular degeneration, diabetic retinopathy, vitreomacular disorders, macular telangiectasia, and inherited retinal dystrophies.
In this review, we outline how even subtle disruptions in Müller cells trophism can drive the pathological cascade of Müller cells pathology, emphasizing the need for targeted therapies to preserve retinal health and prevent disease progression.
{"title":"Müller cells trophism and pathology as the next therapeutic targets for retinal diseases","authors":"Alessandro Arrigo , Ottavio Cremona , Emanuela Aragona , Filippo Casoni , Giacomo Consalez , Rüya Merve Dogru , Stefanie M. Hauck , Alessio Antropoli , Lorenzo Bianco , Maurizio Battaglia Parodi , Francesco Bandello , Antje Grosche","doi":"10.1016/j.preteyeres.2025.101357","DOIUrl":"10.1016/j.preteyeres.2025.101357","url":null,"abstract":"<div><div>Müller cells are a crucial retinal cell type involved in multiple regulatory processes and functions that are essential for retinal health and functionality. Acting as structural and functional support for retinal neurons and photoreceptors, Müller cells produce growth factors, regulate ion and fluid homeostasis, and facilitate neuronal signaling. They play a pivotal role in retinal morphogenesis and cell differentiation, significantly contributing to macular development.</div><div>Due to their radial morphology and unique cytoskeletal organization, Müller cells act as optical fibers, efficiently channeling photons directly to the photoreceptors. In response to retinal damage, Müller cells undergo specific gene expression and functional changes that serve as a first line of defense for neurons, but can also lead to unwarranted cell dysfunction, contributing to cell death and neurodegeneration. In some species, Müller cells can reactivate their developmental program, promoting retinal regeneration and plasticity—a remarkable ability that holds promising therapeutic potential if harnessed in mammals.</div><div>The crucial and multifaceted roles of Müller cells—that we propose to collectively call “Müller cells trophism\"—highlight the necessity of maintaining their functionality. Dysfunction of Müller cells, termed “Müller cells pathology,” has been associated with a plethora of retinal diseases, including age-related macular degeneration, diabetic retinopathy, vitreomacular disorders, macular telangiectasia, and inherited retinal dystrophies.</div><div>In this review, we outline how even subtle disruptions in Müller cells trophism can drive the pathological cascade of Müller cells pathology, emphasizing the need for targeted therapies to preserve retinal health and prevent disease progression.</div></div>","PeriodicalId":21159,"journal":{"name":"Progress in Retinal and Eye Research","volume":"106 ","pages":"Article 101357"},"PeriodicalIF":18.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-18DOI: 10.1016/j.preteyeres.2025.101358
Joshua Ong , Amrish Selvam , Matthew Driban , Arman Zarnegar , Susana Isabel Morgado Mendes Antunes Da Silva , Jincy Joy , Ethan A. Rossi , Jonathan Pieter Vande Geest , José-Alain Sahel , Jay Chhablani
The Bruch's membrane (BM) is an acellular, extracellular matrix that lies between the choroid and retinal pigment epithelium (RPE). The BM plays a critical role in retinal health, performing various functions including biomolecule diffusion and RPE support. The BM is also involved in many retinal diseases, and insights into BM dysfunction allow for further understanding of the pathophysiology of various chorioretinal pathologies. Thus, characterization of the BM serves as an important area of research to further understand its involvement in retinal disease. In this article, we provide a review of various advancements in characterizing and visualizing the BM. We provide an overview of the BM in retinal health, as well as changes observed in aging and disease. We then describe current state-of-the-art imaging modalities and advances to further visualize the BM including various types of optical coherence tomography imaging, near-infrared reflectance (NIR), and autofluorescence imaging and tissue matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS). Following advances in imaging of the BM, we describe animal, cellular, and synthetic models that have been developed to further visualize the BM. Following this section, we provide an overview of deep learning in retinal imaging and describe advances in computational and artificial intelligence (AI) techniques to provide automated segmentation of the BM and BM opening. We conclude this section considering the clinical implications of these segmentation techniques. Ultimately, the diverse advances aimed to further characterize the BM may allow for deeper insights into the involvement of this critical structure in retinal health and disease.
{"title":"Characterizing Bruch's membrane: State-of-the-art imaging, computational segmentation, and biologic models in retinal disease and health","authors":"Joshua Ong , Amrish Selvam , Matthew Driban , Arman Zarnegar , Susana Isabel Morgado Mendes Antunes Da Silva , Jincy Joy , Ethan A. Rossi , Jonathan Pieter Vande Geest , José-Alain Sahel , Jay Chhablani","doi":"10.1016/j.preteyeres.2025.101358","DOIUrl":"10.1016/j.preteyeres.2025.101358","url":null,"abstract":"<div><div>The Bruch's membrane (BM) is an acellular, extracellular matrix that lies between the choroid and retinal pigment epithelium (RPE). The BM plays a critical role in retinal health, performing various functions including biomolecule diffusion and RPE support. The BM is also involved in many retinal diseases, and insights into BM dysfunction allow for further understanding of the pathophysiology of various chorioretinal pathologies. Thus, characterization of the BM serves as an important area of research to further understand its involvement in retinal disease. In this article, we provide a review of various advancements in characterizing and visualizing the BM. We provide an overview of the BM in retinal health, as well as changes observed in aging and disease. We then describe current state-of-the-art imaging modalities and advances to further visualize the BM including various types of optical coherence tomography imaging, near-infrared reflectance (NIR), and autofluorescence imaging and tissue matrix-assisted laser desorption/ionization imaging mass spectrometry (MALDI-IMS). Following advances in imaging of the BM, we describe animal, cellular, and synthetic models that have been developed to further visualize the BM. Following this section, we provide an overview of deep learning in retinal imaging and describe advances in computational and artificial intelligence (AI) techniques to provide automated segmentation of the BM and BM opening. We conclude this section considering the clinical implications of these segmentation techniques. Ultimately, the diverse advances aimed to further characterize the BM may allow for deeper insights into the involvement of this critical structure in retinal health and disease.</div></div>","PeriodicalId":21159,"journal":{"name":"Progress in Retinal and Eye Research","volume":"106 ","pages":"Article 101358"},"PeriodicalIF":18.6,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143859034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-14DOI: 10.1016/j.preteyeres.2025.101356
Biju B. Thomas , Deepthi S. Rajendran Nair , Mana Rahimian , Amr K. Hassan , Thuy-Linh Tran , Magdalene J. Seiler
Retinal degeneration (RD) diseases leading to severe vision loss can affect photoreceptors (PRs) that are responsible for phototransduction, or retinal pigmented epithelium (RPE) providing support for PRs. Human pluripotent stem cell (hPSC)-based therapies are a potential approach for restoration of retinal structure in patients with currently incurable RD diseases. Currently, there are two targeted hPSC therapeutics: PR rescue and PR replacement. PR rescue involves the transplantation of RPE or other neural progenitors into the subretinal space to slow down or prevent further RD. RPE transplantation plays a critical role in preserving photoreceptors by providing trophic support and maintaining retinal integrity, particularly in diseases like age-related macular degeneration (AMD). Advances in RPE transplantation methods, such as polarized monolayer cultures and scaffold-based approaches, have shown promise in enhancing graft survival and integration. However, limitations include inconsistent integration, variable neurotrophic factor secretion, and immune rejection risks in non-autologous transplants. In PR replacement, stem cell-derived photoreceptor-like cells or photoreceptor progenitors (PRP) obtained are transplanted into the eye. While PRPs are commonly obtained from retinal organoids (ROs), alternative sources, such as early differentiation stages or direct differentiation protocols, are also utilized to enhance the efficiency and scalability of PRP generation. Challenges include achieving proper integration, forming outer segments, rosette formation, and avoiding immune rejection or tumorigenicity. Various animal models that simulate human RD diseases are being used for establishing surgical feasibility, graft survival and visual functional recovery but fail to replicate clinical immune challenges. Rodent models lack macula-like structures and have limited reliability in detecting subtle functional changes, while larger animal models pose ethical, logistical, and financial challenges. Immunocompromised models have been developed for minimizing xenograft issues. Visual functional testing for efficacy includes optokinetic testing (OKN), electroretinography (ERG), and electrophysiological recordings from the retina and brain. These tests often fail to capture the complexity of human visual recovery, highlighting the need for advanced models and improved functional testing techniques. This review aims to aggregate current knowledge about approaches to stem cell transplantation, requirements of animal models chosen for validating vision benefits of transplantation studies, advantages of using specific disease models and their limitations. While promising strides have been made, addressing these limitations remains essential for translating stem cell-based therapies into clinical success.
{"title":"Animal models for the evaluation of retinal stem cell therapies","authors":"Biju B. Thomas , Deepthi S. Rajendran Nair , Mana Rahimian , Amr K. Hassan , Thuy-Linh Tran , Magdalene J. Seiler","doi":"10.1016/j.preteyeres.2025.101356","DOIUrl":"10.1016/j.preteyeres.2025.101356","url":null,"abstract":"<div><div>Retinal degeneration (RD) diseases leading to severe vision loss can affect photoreceptors (PRs) that are responsible for phototransduction, or retinal pigmented epithelium (RPE) providing support for PRs. Human pluripotent stem cell (hPSC)-based therapies are a potential approach for restoration of retinal structure in patients with currently incurable RD diseases. Currently, there are two targeted hPSC therapeutics: PR rescue and PR replacement. PR rescue involves the transplantation of RPE or other neural progenitors into the subretinal space to slow down or prevent further RD. RPE transplantation plays a critical role in preserving photoreceptors by providing trophic support and maintaining retinal integrity, particularly in diseases like age-related macular degeneration (AMD). Advances in RPE transplantation methods, such as polarized monolayer cultures and scaffold-based approaches, have shown promise in enhancing graft survival and integration. However, limitations include inconsistent integration, variable neurotrophic factor secretion, and immune rejection risks in non-autologous transplants. In PR replacement, stem cell-derived photoreceptor-like cells or photoreceptor progenitors (PRP) obtained are transplanted into the eye. While PRPs are commonly obtained from retinal organoids (ROs), alternative sources, such as early differentiation stages or direct differentiation protocols, are also utilized to enhance the efficiency and scalability of PRP generation. Challenges include achieving proper integration, forming outer segments, rosette formation, and avoiding immune rejection or tumorigenicity. Various animal models that simulate human RD diseases are being used for establishing surgical feasibility, graft survival and visual functional recovery but fail to replicate clinical immune challenges. Rodent models lack macula-like structures and have limited reliability in detecting subtle functional changes, while larger animal models pose ethical, logistical, and financial challenges. Immunocompromised models have been developed for minimizing xenograft issues. Visual functional testing for efficacy includes optokinetic testing (OKN), electroretinography (ERG), and electrophysiological recordings from the retina and brain. These tests often fail to capture the complexity of human visual recovery, highlighting the need for advanced models and improved functional testing techniques. This review aims to aggregate current knowledge about approaches to stem cell transplantation, requirements of animal models chosen for validating vision benefits of transplantation studies, advantages of using specific disease models and their limitations. While promising strides have been made, addressing these limitations remains essential for translating stem cell-based therapies into clinical success.</div></div>","PeriodicalId":21159,"journal":{"name":"Progress in Retinal and Eye Research","volume":"106 ","pages":"Article 101356"},"PeriodicalIF":18.6,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143865092","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-04-02DOI: 10.1016/j.preteyeres.2025.101355
Chieh-Lin Stanley Wu, Adrian V Cioanca, Maria C Gelmi, Li Wen, Nick Di Girolamo, Ling Zhu, Riccardo Natoli, R Max Conway, Constantinos Petsoglou, Martine J Jager, Peter J McCluskey, Michele C Madigan
{"title":"Corrigendum to \"The multifunctional human ocular melanocortin system\" [Prog. Retin. Eye Res. 95 (2023) 1-23 101187].","authors":"Chieh-Lin Stanley Wu, Adrian V Cioanca, Maria C Gelmi, Li Wen, Nick Di Girolamo, Ling Zhu, Riccardo Natoli, R Max Conway, Constantinos Petsoglou, Martine J Jager, Peter J McCluskey, Michele C Madigan","doi":"10.1016/j.preteyeres.2025.101355","DOIUrl":"https://doi.org/10.1016/j.preteyeres.2025.101355","url":null,"abstract":"","PeriodicalId":21159,"journal":{"name":"Progress in Retinal and Eye Research","volume":" ","pages":"101355"},"PeriodicalIF":18.6,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143780960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-17DOI: 10.1016/j.preteyeres.2025.101353
Benjamin Phipps , Xavier Hadoux , Bin Sheng , J. Peter Campbell , T.Y. Alvin Liu , Pearse A. Keane , Carol Y. Cheung , Tham Yih Chung , Tien Y. Wong , Peter van Wijngaarden
Background
AI-powered image generation technology holds the potential to reshape medical practice, yet it remains an unfamiliar technology for both medical researchers and clinicians alike. Given the adoption of this technology relies on clinician understanding and acceptance, we sought to demystify its use in ophthalmology. To this end, we present a literature review on image generation technology in ophthalmology, examining both its theoretical applications and future role in clinical practice.
Methods
First, we consider the key model designs used for image synthesis, including generative adversarial networks, autoencoders, and diffusion models. We then perform a survey of the literature for image generation technology in ophthalmology prior to September 2024, presenting both the type of model used and its clinical application. Finally, we discuss the limitations of this technology, the risks of its misuse and the future directions of research in this field.
Results
Applications of this technology include improving AI diagnostic models, inter-modality image transformation, more accurate treatment and disease prognostication, image denoising, and individualised education. Key barriers to its adoption include bias in generative models, risks to patient data security, computational and logistical barriers to development, challenges with model explainability, inconsistent use of validation metrics between studies and misuse of synthetic images. Looking forward, researchers are placing a further emphasis on clinically grounded metrics, the development of image generation foundation models and the implementation of methods to ensure data provenance.
Conclusion
Compared to other medical applications of AI, image generation is still in its infancy. Yet, it holds the potential to revolutionise ophthalmology across research, education and clinical practice. This review aims to guide ophthalmic researchers wanting to leverage this technology, while also providing an insight for clinicians on how it may change ophthalmic practice in the future.
{"title":"AI image generation technology in ophthalmology: Use, misuse and future applications","authors":"Benjamin Phipps , Xavier Hadoux , Bin Sheng , J. Peter Campbell , T.Y. Alvin Liu , Pearse A. Keane , Carol Y. Cheung , Tham Yih Chung , Tien Y. Wong , Peter van Wijngaarden","doi":"10.1016/j.preteyeres.2025.101353","DOIUrl":"10.1016/j.preteyeres.2025.101353","url":null,"abstract":"<div><h3>Background</h3><div>AI-powered image generation technology holds the potential to reshape medical practice, yet it remains an unfamiliar technology for both medical researchers and clinicians alike. Given the adoption of this technology relies on clinician understanding and acceptance, we sought to demystify its use in ophthalmology. To this end, we present a literature review on image generation technology in ophthalmology, examining both its theoretical applications and future role in clinical practice.</div></div><div><h3>Methods</h3><div>First, we consider the key model designs used for image synthesis, including generative adversarial networks, autoencoders, and diffusion models. We then perform a survey of the literature for image generation technology in ophthalmology prior to September 2024, presenting both the type of model used and its clinical application. Finally, we discuss the limitations of this technology, the risks of its misuse and the future directions of research in this field.</div></div><div><h3>Results</h3><div>Applications of this technology include improving AI diagnostic models, inter-modality image transformation, more accurate treatment and disease prognostication, image denoising, and individualised education. Key barriers to its adoption include bias in generative models, risks to patient data security, computational and logistical barriers to development, challenges with model explainability, inconsistent use of validation metrics between studies and misuse of synthetic images. Looking forward, researchers are placing a further emphasis on clinically grounded metrics, the development of image generation foundation models and the implementation of methods to ensure data provenance.</div></div><div><h3>Conclusion</h3><div>Compared to other medical applications of AI, image generation is still in its infancy. Yet, it holds the potential to revolutionise ophthalmology across research, education and clinical practice. This review aims to guide ophthalmic researchers wanting to leverage this technology, while also providing an insight for clinicians on how it may change ophthalmic practice in the future.</div></div>","PeriodicalId":21159,"journal":{"name":"Progress in Retinal and Eye Research","volume":"106 ","pages":"Article 101353"},"PeriodicalIF":18.6,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-14DOI: 10.1016/j.preteyeres.2025.101354
Ryan Purdy , Molly John , Alissa Bray , Alison J. Clare , David A. Copland , Ying Kai Chan , Robert H. Henderson , Fanny Nerinckx , Bart P. Leroy , Paul Yang , Mark E. Pennesi , Robert E. MacLaren , M Dominik Fischer , Andrew D. Dick , Kanmin Xue
Retinal gene therapy using adeno-associated viral (AAV) vectors has been a groundbreaking step-change in the treatment of inherited retinal diseases (IRDs) and could also be used to treat more common retinal diseases such as age-related macular degeneration and diabetic retinopathy. The delivery and expression of therapeutic transgenes in the eye is limited by innate and adaptive immune responses against components of the vector product, which has been termed gene therapy-associated uveitis (GTAU). This is clinically important as intraocular inflammation could lead to irreversible loss of retinal cells, deterioration of visual function and reduced durability of treatment effect associated with a costly one-off treatment. For retinal gene therapy to achieve an improved efficacy and safety profile for treating additional IRDs and more common diseases, the risk of GTAU must be minimised. We have collated insights from pre-clinical research, clinical trials, and the real-world implementation of AAV-mediated retinal gene therapy to help understand the risk factors for GTAU. We draw attention to an emerging framework, which includes patient demographics, vector construct, vector dose, route of administration, and choice of immunosuppression regime. Importantly, we consider efforts to date and potential future strategies to mitigate the adverse immune response across each of these domains. We advocate for more targeted immunomodulatory approaches to the prevention and treatment of GTAU based on better understanding of the underlying immune response.
{"title":"Gene Therapy-Associated Uveitis (GTAU): Understanding and mitigating the adverse immune response in retinal gene therapy","authors":"Ryan Purdy , Molly John , Alissa Bray , Alison J. Clare , David A. Copland , Ying Kai Chan , Robert H. Henderson , Fanny Nerinckx , Bart P. Leroy , Paul Yang , Mark E. Pennesi , Robert E. MacLaren , M Dominik Fischer , Andrew D. Dick , Kanmin Xue","doi":"10.1016/j.preteyeres.2025.101354","DOIUrl":"10.1016/j.preteyeres.2025.101354","url":null,"abstract":"<div><div>Retinal gene therapy using adeno-associated viral (AAV) vectors has been a groundbreaking step-change in the treatment of inherited retinal diseases (IRDs) and could also be used to treat more common retinal diseases such as age-related macular degeneration and diabetic retinopathy. The delivery and expression of therapeutic transgenes in the eye is limited by innate and adaptive immune responses against components of the vector product, which has been termed gene therapy-associated uveitis (GTAU). This is clinically important as intraocular inflammation could lead to irreversible loss of retinal cells, deterioration of visual function and reduced durability of treatment effect associated with a costly one-off treatment. For retinal gene therapy to achieve an improved efficacy and safety profile for treating additional IRDs and more common diseases, the risk of GTAU must be minimised. We have collated insights from pre-clinical research, clinical trials, and the real-world implementation of AAV-mediated retinal gene therapy to help understand the risk factors for GTAU. We draw attention to an emerging framework, which includes patient demographics, vector construct, vector dose, route of administration, and choice of immunosuppression regime. Importantly, we consider efforts to date and potential future strategies to mitigate the adverse immune response across each of these domains. We advocate for more targeted immunomodulatory approaches to the prevention and treatment of GTAU based on better understanding of the underlying immune response.</div></div>","PeriodicalId":21159,"journal":{"name":"Progress in Retinal and Eye Research","volume":"106 ","pages":"Article 101354"},"PeriodicalIF":18.6,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-12DOI: 10.1016/j.preteyeres.2025.101352
Songyang An , Kelvin Teo , Michael V. McConnell , John Marshall , Christopher Galloway , David Squirrell
Recent developments in artificial intelligence (AI) have seen a proliferation of algorithms that are now capable of predicting a range of systemic diseases from retinal images. Unlike traditional retinal disease detection AI models which are trained on well-recognised retinal biomarkers, systemic disease detection or “oculomics” models use a range of often poorly characterised retinal biomarkers to arrive at their predictions. As the retinal phenotype that oculomics models use may not be intuitive, clinicians have to rely on the developers’ explanations of how these algorithms work in order to understand them. The discipline of understanding how AI algorithms work employs two similar but distinct terms: Explainable AI and Interpretable AI (iAI). Explainable AI describes the holistic functioning of an AI system, including its impact and potential biases. Interpretable AI concentrates solely on examining and understanding the workings of the AI algorithm itself. iAI tools are therefore what the clinician must rely on if they are to understand how the algorithm works and whether its predictions are reliable. The iAI tools that developers use can be delineated into two broad categories: Intrinsic methods that improve transparency through architectural changes and post-hoc methods that explain trained models via external algorithms. Currently post-hoc methods, class activation maps in particular, are far more widely used than other techniques but they have their limitations especially when applied to oculomics AI models. Aimed at clinicians, we examine how the key iAI methods work, what they are designed to do and what their limitations are when applied to oculomics AI. We conclude by discussing how combining existing iAI techniques with novel approaches could allow AI developers to better explain how their oculomics models work and reassure clinicians that the results issued are reliable.
{"title":"AI explainability in oculomics: How it works, its role in establishing trust, and what still needs to be addressed","authors":"Songyang An , Kelvin Teo , Michael V. McConnell , John Marshall , Christopher Galloway , David Squirrell","doi":"10.1016/j.preteyeres.2025.101352","DOIUrl":"10.1016/j.preteyeres.2025.101352","url":null,"abstract":"<div><div>Recent developments in artificial intelligence (AI) have seen a proliferation of algorithms that are now capable of predicting a range of systemic diseases from retinal images. Unlike traditional retinal disease detection AI models which are trained on well-recognised retinal biomarkers, systemic disease detection or “oculomics” models use a range of often poorly characterised retinal biomarkers to arrive at their predictions. As the retinal phenotype that oculomics models use may not be intuitive, clinicians have to rely on the developers’ explanations of how these algorithms work in order to understand them. The discipline of understanding how AI algorithms work employs two similar but distinct terms: Explainable AI and Interpretable AI (iAI). Explainable AI describes the holistic functioning of an AI system, including its impact and potential biases. Interpretable AI concentrates solely on examining and understanding the workings of the AI algorithm itself. iAI tools are therefore what the clinician must rely on if they are to understand how the algorithm works and whether its predictions are reliable. The iAI tools that developers use can be delineated into two broad categories: Intrinsic methods that improve transparency through architectural changes and post-hoc methods that explain trained models via external algorithms. Currently post-hoc methods, class activation maps in particular, are far more widely used than other techniques but they have their limitations especially when applied to oculomics AI models. Aimed at clinicians, we examine how the key iAI methods work, what they are designed to do and what their limitations are when applied to oculomics AI. We conclude by discussing how combining existing iAI techniques with novel approaches could allow AI developers to better explain how their oculomics models work and reassure clinicians that the results issued are reliable.</div></div>","PeriodicalId":21159,"journal":{"name":"Progress in Retinal and Eye Research","volume":"106 ","pages":"Article 101352"},"PeriodicalIF":18.6,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630829","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-05DOI: 10.1016/j.preteyeres.2025.101351
Alessandro Feo , Prithvi Ramtohul , Andrea Govetto , Enrico Borrelli , Riccardo Sacconi , Giulia Corradetti , Giuseppe Querques , Mario R. Romano , Philip J. Rosenfeld , Richard F. Spaide , K Bailey Freund , SriniVas Sadda , David Sarraf
En face optical coherence tomography (OCT) is a practical and informative imaging modality to noninvasively visualize distinct retinal and choroidal layers by providing coronal images using boundary-specific segmentation. Ongoing research with this method is generating breakthroughs in the illustration of new perspectives of retinal disease. The clinical value of en face OCT as an advanced retinal imaging tool is growing steadily and it has unveiled many new insights into the pathoanatomy of retinal disorders. Moreover, this modality can capture various en face OCT biomarkers that correspond to different cell or tissue subtypes, which were previously only identified through histological or electron microscopy methods, underscoring the significance of this technique in providing valuable pathoanatomical information.
In this comprehensive review, we will systematically summarize the en face OCT findings across a broad spectrum of retinal diseases, including disorders of the vitreoretinal interface and retinal vascular system (e.g. paracentral acute middle maculopathy or PAMM and diabetic retinopathy), in addition to the en face OCT features of other conditions such as age-related macular degeneration, pachychoroid disease spectrum, myopic degeneration, uveitis and inflammatory disorders, inherited retinal dystrophies, and drug toxicity. We will discuss and highlight the unique clinical and pathoanatomical findings uncovered with en face OCT of each these diseases mentioned above.
{"title":"En face OCT: Breakthroughs in understanding the pathoanatomy of retinal disease and clinical applications","authors":"Alessandro Feo , Prithvi Ramtohul , Andrea Govetto , Enrico Borrelli , Riccardo Sacconi , Giulia Corradetti , Giuseppe Querques , Mario R. Romano , Philip J. Rosenfeld , Richard F. Spaide , K Bailey Freund , SriniVas Sadda , David Sarraf","doi":"10.1016/j.preteyeres.2025.101351","DOIUrl":"10.1016/j.preteyeres.2025.101351","url":null,"abstract":"<div><div>En face optical coherence tomography (OCT) is a practical and informative imaging modality to noninvasively visualize distinct retinal and choroidal layers by providing coronal images using boundary-specific segmentation. Ongoing research with this method is generating breakthroughs in the illustration of new perspectives of retinal disease. The clinical value of en face OCT as an advanced retinal imaging tool is growing steadily and it has unveiled many new insights into the pathoanatomy of retinal disorders. Moreover, this modality can capture various en face OCT biomarkers that correspond to different cell or tissue subtypes, which were previously only identified through histological or electron microscopy methods, underscoring the significance of this technique in providing valuable pathoanatomical information.</div><div>In this comprehensive review, we will systematically summarize the en face OCT findings across a broad spectrum of retinal diseases, including disorders of the vitreoretinal interface and retinal vascular system (e.g. paracentral acute middle maculopathy or PAMM and diabetic retinopathy), in addition to the en face OCT features of other conditions such as age-related macular degeneration, pachychoroid disease spectrum, myopic degeneration, uveitis and inflammatory disorders, inherited retinal dystrophies, and drug toxicity. We will discuss and highlight the unique clinical and pathoanatomical findings uncovered with en face OCT of each these diseases mentioned above.</div></div>","PeriodicalId":21159,"journal":{"name":"Progress in Retinal and Eye Research","volume":"106 ","pages":"Article 101351"},"PeriodicalIF":18.6,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143586585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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