Pub Date : 2024-09-01Epub Date: 2024-06-26DOI: 10.1097/ICU.0000000000001071
Ron Kaufman, Albert S Jun
Purpose of review: While effective for treating endothelial dysfunction, keratoplasty has shortcomings including limited access to donor tissue for much of the world. Thus, alternative strategies are under development. This review explores the main advancements achieved in this field during 2022-2023.
Recent findings: Recent publications further support the validity of intracameral cultivated allogeneic endothelial cell injection and Descemet stripping only, while emphasizing the benefits of adjunctive Rho-associated kinase inhibitor (ROCKi) therapy. New donor-independent artificial implants, such as EndoArt, show favorable results. Multiple pharmacologic agents, especially ROCKi, show promise as monotherapies, yet none are currently approved for human treatment. Multiple regenerative and genetic therapies are being investigated but all are still in preclinical stages.
Summary: A plethora of innovative alternatives to keratoplasty for endothelial disease is in development. Among these, surgical methods are still the mainstay of treatment and closest to clinical application, though further studies to establish their benefits over keratoplasty are needed. Albeit promising, pharmacologic, regenerative, and genetic approaches require validation and are farther from clinical application.
{"title":"Emerging alternatives to keratoplasty for corneal endothelial cell dysfunction.","authors":"Ron Kaufman, Albert S Jun","doi":"10.1097/ICU.0000000000001071","DOIUrl":"10.1097/ICU.0000000000001071","url":null,"abstract":"<p><strong>Purpose of review: </strong>While effective for treating endothelial dysfunction, keratoplasty has shortcomings including limited access to donor tissue for much of the world. Thus, alternative strategies are under development. This review explores the main advancements achieved in this field during 2022-2023.</p><p><strong>Recent findings: </strong>Recent publications further support the validity of intracameral cultivated allogeneic endothelial cell injection and Descemet stripping only, while emphasizing the benefits of adjunctive Rho-associated kinase inhibitor (ROCKi) therapy. New donor-independent artificial implants, such as EndoArt, show favorable results. Multiple pharmacologic agents, especially ROCKi, show promise as monotherapies, yet none are currently approved for human treatment. Multiple regenerative and genetic therapies are being investigated but all are still in preclinical stages.</p><p><strong>Summary: </strong>A plethora of innovative alternatives to keratoplasty for endothelial disease is in development. Among these, surgical methods are still the mainstay of treatment and closest to clinical application, though further studies to establish their benefits over keratoplasty are needed. Albeit promising, pharmacologic, regenerative, and genetic approaches require validation and are farther from clinical application.</p>","PeriodicalId":50604,"journal":{"name":"Current Opinion in Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141472187","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-09-01Epub Date: 2024-08-08DOI: 10.1097/ICU.0000000000001073
Avni P Finn, Jayanth Sridhar
{"title":"Challenges and controversies in ophthalmology in 2024.","authors":"Avni P Finn, Jayanth Sridhar","doi":"10.1097/ICU.0000000000001073","DOIUrl":"https://doi.org/10.1097/ICU.0000000000001073","url":null,"abstract":"","PeriodicalId":50604,"journal":{"name":"Current Opinion in Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141903469","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-29DOI: 10.1097/icu.0000000000001088
Mark E Pennesi,Yi-Zhong Wang,David G Birch
PURPOSE OF REVIEWThe purpose of this review was to provide a summary of currently available retinal imaging and visual function testing methods for assessing inherited retinal degenerations (IRDs), with the emphasis on the application of deep learning (DL) approaches to assist the determination of structural biomarkers for IRDs.RECENT FINDINGS(clinical trials for IRDs; discover effective biomarkers as endpoints; DL applications in processing retinal images to detect disease-related structural changes).SUMMARYAssessing photoreceptor loss is a direct way to evaluate IRDs. Outer retinal layer structures, including outer nuclear layer, ellipsoid zone, photoreceptor outer segment, RPE, are potential structural biomarkers for IRDs. More work may be needed on structure and function relationship.
{"title":"Deep learning aided measurement of outer retinal layer metrics as biomarkers for inherited retinal degenerations: opportunities and challenges.","authors":"Mark E Pennesi,Yi-Zhong Wang,David G Birch","doi":"10.1097/icu.0000000000001088","DOIUrl":"https://doi.org/10.1097/icu.0000000000001088","url":null,"abstract":"PURPOSE OF REVIEWThe purpose of this review was to provide a summary of currently available retinal imaging and visual function testing methods for assessing inherited retinal degenerations (IRDs), with the emphasis on the application of deep learning (DL) approaches to assist the determination of structural biomarkers for IRDs.RECENT FINDINGS(clinical trials for IRDs; discover effective biomarkers as endpoints; DL applications in processing retinal images to detect disease-related structural changes).SUMMARYAssessing photoreceptor loss is a direct way to evaluate IRDs. Outer retinal layer structures, including outer nuclear layer, ellipsoid zone, photoreceptor outer segment, RPE, are potential structural biomarkers for IRDs. More work may be needed on structure and function relationship.","PeriodicalId":50604,"journal":{"name":"Current Opinion in Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-28DOI: 10.1097/icu.0000000000001090
Radhika Rampat,Guillaume Debellemanière,Damien Gatinel,Darren S J Ting
PURPOSE OF REVIEWThis review highlights the recent advancements in the applications of artificial intelligence within the field of cataract and refractive surgeries. Given the rapid evolution of artificial intelligence technologies, it is essential to provide an updated overview of the significant strides and emerging trends in this field.RECENT FINDINGSKey themes include artificial intelligence-assisted diagnostics and intraoperative support, image analysis for anterior segment surgeries, development of artificial intelligence-based diagnostic scores and calculators for early disease detection and treatment planning, and integration of generative artificial intelligence for patient education and postoperative monitoring.SUMMARYThe impact of artificial intelligence on cataract and refractive surgeries is becoming increasingly evident through improved diagnostic accuracy, enhanced patient education, and streamlined clinical workflows. These advancements hold significant implications for clinical practice, promising more personalized patient care and facilitating early disease detection and intervention. Equally, the review also highlights the fact that only some of this work reaches the clinical stage, successful integration of which may benefit from our focus.
{"title":"Artificial intelligence applications in cataract and refractive surgeries.","authors":"Radhika Rampat,Guillaume Debellemanière,Damien Gatinel,Darren S J Ting","doi":"10.1097/icu.0000000000001090","DOIUrl":"https://doi.org/10.1097/icu.0000000000001090","url":null,"abstract":"PURPOSE OF REVIEWThis review highlights the recent advancements in the applications of artificial intelligence within the field of cataract and refractive surgeries. Given the rapid evolution of artificial intelligence technologies, it is essential to provide an updated overview of the significant strides and emerging trends in this field.RECENT FINDINGSKey themes include artificial intelligence-assisted diagnostics and intraoperative support, image analysis for anterior segment surgeries, development of artificial intelligence-based diagnostic scores and calculators for early disease detection and treatment planning, and integration of generative artificial intelligence for patient education and postoperative monitoring.SUMMARYThe impact of artificial intelligence on cataract and refractive surgeries is becoming increasingly evident through improved diagnostic accuracy, enhanced patient education, and streamlined clinical workflows. These advancements hold significant implications for clinical practice, promising more personalized patient care and facilitating early disease detection and intervention. Equally, the review also highlights the fact that only some of this work reaches the clinical stage, successful integration of which may benefit from our focus.","PeriodicalId":50604,"journal":{"name":"Current Opinion in Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1097/icu.0000000000001085
Marie Louise Enzendorfer,Ursula Schmidt-Erfurth
PURPOSE OF REVIEWThis review aims to address the recent advances of artificial intelligence (AI) in the context of clinical management of geographic atrophy (GA), a vision-impairing late-stage manifestation of age-related macular degeneration (AMD).RECENT FINDINGSRecent literature shows substantial advancements in the development of AI systems to segment GA lesions on multimodal retinal images, including color fundus photography (CFP), fundus autofluorescence (FAF) and optical coherence tomography (OCT), providing innovative solutions to screening and early diagnosis. Especially, the high resolution and 3D-nature of OCT has provided an optimal source of data for the training and validation of novel algorithms. The use of AI to measure progression in the context of newly approved GA therapies, has shown that AI methods may soon be indispensable for patient management. To date, while many AI models have been reported on, their implementation in the real-world has only just started. The aim is to make the benefits of AI-based personalized treatment accessible and far-reaching.SUMMARYThe most recent advances (pearls) and challenges (pitfalls) associated with AI methods and their clinical implementation in the context of GA will be discussed.
综述目的本综述旨在探讨人工智能（AI）在地理萎缩（GA）临床管理方面的最新进展，地理萎缩是老年性黄斑变性（AMD）晚期的一种视力损害表现。最近的发现最近的文献显示，在开发人工智能系统方面取得了重大进展，该系统可对多模态视网膜图像（包括彩色眼底照相（CFP）、眼底自动荧光（FAF）和光学相干断层扫描（OCT））上的GA病变进行分割，为筛查和早期诊断提供了创新解决方案。尤其是光学相干断层扫描的高分辨率和三维特性，为新型算法的训练和验证提供了最佳数据来源。在新批准的 GA 疗法中使用人工智能来测量病情进展，这表明人工智能方法可能很快就会成为患者管理中不可或缺的手段。迄今为止，虽然已有许多人工智能模型被报道，但它们在现实世界中的应用才刚刚开始。摘要 将讨论人工智能方法的最新进展（珍珠）和挑战（陷阱），以及它们在天基疗法中的临床应用。
{"title":"Artificial intelligence for geographic atrophy: pearls and pitfalls.","authors":"Marie Louise Enzendorfer,Ursula Schmidt-Erfurth","doi":"10.1097/icu.0000000000001085","DOIUrl":"https://doi.org/10.1097/icu.0000000000001085","url":null,"abstract":"PURPOSE OF REVIEWThis review aims to address the recent advances of artificial intelligence (AI) in the context of clinical management of geographic atrophy (GA), a vision-impairing late-stage manifestation of age-related macular degeneration (AMD).RECENT FINDINGSRecent literature shows substantial advancements in the development of AI systems to segment GA lesions on multimodal retinal images, including color fundus photography (CFP), fundus autofluorescence (FAF) and optical coherence tomography (OCT), providing innovative solutions to screening and early diagnosis. Especially, the high resolution and 3D-nature of OCT has provided an optimal source of data for the training and validation of novel algorithms. The use of AI to measure progression in the context of newly approved GA therapies, has shown that AI methods may soon be indispensable for patient management. To date, while many AI models have been reported on, their implementation in the real-world has only just started. The aim is to make the benefits of AI-based personalized treatment accessible and far-reaching.SUMMARYThe most recent advances (pearls) and challenges (pitfalls) associated with AI methods and their clinical implementation in the context of GA will be discussed.","PeriodicalId":50604,"journal":{"name":"Current Opinion in Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1097/icu.0000000000001086
Clarissa Ng Yin Ling,Xiangjia Zhu,Marcus Ang
PURPOSE OF REVIEWMyopia is one of the major causes of visual impairment globally, with myopia and its complications thus placing a heavy healthcare and economic burden. With most cases of myopia developing during childhood, interventions to slow myopia progression are most effective when implemented early. To address this public health challenge, artificial intelligence has emerged as a potential solution in childhood myopia management.RECENT FINDINGSThe bulk of artificial intelligence research in childhood myopia was previously focused on traditional machine learning models for the identification of children at high risk for myopia progression. Recently, there has been a surge of literature with larger datasets, more computational power, and more complex computation models, leveraging artificial intelligence for novel approaches including large-scale myopia screening using big data, multimodal data, and advancing imaging technology for myopia progression, and deep learning models for precision treatment.SUMMARYArtificial intelligence holds significant promise in transforming the field of childhood myopia management. Novel artificial intelligence modalities including automated machine learning, large language models, and federated learning could play an important role in the future by delivering precision medicine, improving health literacy, and allowing the preservation of data privacy. However, along with these advancements in technology come practical challenges including regulation and clinical integration.
{"title":"Artificial intelligence in myopia in children: current trends and future directions.","authors":"Clarissa Ng Yin Ling,Xiangjia Zhu,Marcus Ang","doi":"10.1097/icu.0000000000001086","DOIUrl":"https://doi.org/10.1097/icu.0000000000001086","url":null,"abstract":"PURPOSE OF REVIEWMyopia is one of the major causes of visual impairment globally, with myopia and its complications thus placing a heavy healthcare and economic burden. With most cases of myopia developing during childhood, interventions to slow myopia progression are most effective when implemented early. To address this public health challenge, artificial intelligence has emerged as a potential solution in childhood myopia management.RECENT FINDINGSThe bulk of artificial intelligence research in childhood myopia was previously focused on traditional machine learning models for the identification of children at high risk for myopia progression. Recently, there has been a surge of literature with larger datasets, more computational power, and more complex computation models, leveraging artificial intelligence for novel approaches including large-scale myopia screening using big data, multimodal data, and advancing imaging technology for myopia progression, and deep learning models for precision treatment.SUMMARYArtificial intelligence holds significant promise in transforming the field of childhood myopia management. Novel artificial intelligence modalities including automated machine learning, large language models, and federated learning could play an important role in the future by delivering precision medicine, improving health literacy, and allowing the preservation of data privacy. However, along with these advancements in technology come practical challenges including regulation and clinical integration.","PeriodicalId":50604,"journal":{"name":"Current Opinion in Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1097/icu.0000000000001084
Stela Vujosevic,Celeste Limoli,Paolo Nucci
PURPOSE OF REVIEWGiven the increasing global burden of diabetic retinopathy and the rapid advancements in artificial intelligence, this review aims to summarize the current state of artificial intelligence technology in diabetic retinopathy detection and management, assessing its potential to improve care and visual outcomes in real-world settings.RECENT FINDINGSMost recent studies focused on the integration of artificial intelligence in the field of diabetic retinopathy screening, focusing on real-world efficacy and clinical implementation of such artificial intelligence models. Additionally, artificial intelligence holds the potential to predict diabetic retinopathy progression, enhance personalized treatment strategies, and identify systemic disease biomarkers from ocular images through 'oculomics', moving towards a more precise, efficient, and accessible care. The emergence of foundation model architectures and generative artificial intelligence, which more clearly reflect the clinical care process, may enable rapid advances in diabetic retinopathy care, research and medical education.SUMMARYThis review explores the emerging technology of artificial intelligence to assess the potential to improve patient outcomes and optimize personalized management in healthcare delivery and medical research. While artificial intelligence is expected to play an increasingly important role in diabetic retinopathy care, ongoing research and clinical trials are essential to address implementation issues and focus on long-term patient outcomes for successful real-world adoption of artificial intelligence in diabetic retinopathy.
{"title":"Novel artificial intelligence for diabetic retinopathy and diabetic macular edema: what is new in 2024?","authors":"Stela Vujosevic,Celeste Limoli,Paolo Nucci","doi":"10.1097/icu.0000000000001084","DOIUrl":"https://doi.org/10.1097/icu.0000000000001084","url":null,"abstract":"PURPOSE OF REVIEWGiven the increasing global burden of diabetic retinopathy and the rapid advancements in artificial intelligence, this review aims to summarize the current state of artificial intelligence technology in diabetic retinopathy detection and management, assessing its potential to improve care and visual outcomes in real-world settings.RECENT FINDINGSMost recent studies focused on the integration of artificial intelligence in the field of diabetic retinopathy screening, focusing on real-world efficacy and clinical implementation of such artificial intelligence models. Additionally, artificial intelligence holds the potential to predict diabetic retinopathy progression, enhance personalized treatment strategies, and identify systemic disease biomarkers from ocular images through 'oculomics', moving towards a more precise, efficient, and accessible care. The emergence of foundation model architectures and generative artificial intelligence, which more clearly reflect the clinical care process, may enable rapid advances in diabetic retinopathy care, research and medical education.SUMMARYThis review explores the emerging technology of artificial intelligence to assess the potential to improve patient outcomes and optimize personalized management in healthcare delivery and medical research. While artificial intelligence is expected to play an increasingly important role in diabetic retinopathy care, ongoing research and clinical trials are essential to address implementation issues and focus on long-term patient outcomes for successful real-world adoption of artificial intelligence in diabetic retinopathy.","PeriodicalId":50604,"journal":{"name":"Current Opinion in Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-27DOI: 10.1097/icu.0000000000001083
Fritz Gerald P Kalaw,Sally L Baxter
PURPOSE OF REVIEWThis review aims to summarize and discuss the ethical considerations regarding large language model (LLM) use in the field of ophthalmology.RECENT FINDINGSThis review of 47 articles on LLM applications in ophthalmology highlights their diverse potential uses, including education, research, clinical decision support, and surgical assistance (as an aid in operative notes). We also review ethical considerations such as the inability of LLMs to interpret data accurately, the risk of promoting controversial or harmful recommendations, and breaches of data privacy. These concerns imply the need for cautious integration of artificial intelligence in healthcare, emphasizing human oversight, transparency, and accountability to mitigate risks and uphold ethical standards.SUMMARYThe integration of LLMs in ophthalmology offers potential advantages such as aiding in clinical decision support and facilitating medical education through their ability to process queries and analyze ophthalmic imaging and clinical cases. However, their utilization also raises ethical concerns regarding data privacy, potential misinformation, and biases inherent in the datasets used. Awareness of these concerns should be addressed in order to optimize its utility in the healthcare setting. More importantly, promoting responsible and careful use by consumers should be practiced.
{"title":"Ethical considerations for large language models in ophthalmology.","authors":"Fritz Gerald P Kalaw,Sally L Baxter","doi":"10.1097/icu.0000000000001083","DOIUrl":"https://doi.org/10.1097/icu.0000000000001083","url":null,"abstract":"PURPOSE OF REVIEWThis review aims to summarize and discuss the ethical considerations regarding large language model (LLM) use in the field of ophthalmology.RECENT FINDINGSThis review of 47 articles on LLM applications in ophthalmology highlights their diverse potential uses, including education, research, clinical decision support, and surgical assistance (as an aid in operative notes). We also review ethical considerations such as the inability of LLMs to interpret data accurately, the risk of promoting controversial or harmful recommendations, and breaches of data privacy. These concerns imply the need for cautious integration of artificial intelligence in healthcare, emphasizing human oversight, transparency, and accountability to mitigate risks and uphold ethical standards.SUMMARYThe integration of LLMs in ophthalmology offers potential advantages such as aiding in clinical decision support and facilitating medical education through their ability to process queries and analyze ophthalmic imaging and clinical cases. However, their utilization also raises ethical concerns regarding data privacy, potential misinformation, and biases inherent in the datasets used. Awareness of these concerns should be addressed in order to optimize its utility in the healthcare setting. More importantly, promoting responsible and careful use by consumers should be practiced.","PeriodicalId":50604,"journal":{"name":"Current Opinion in Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142223632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-08-15DOI: 10.1097/ICU.0000000000001033
Yannek I Leiderman, Matthew J Gerber, Jean-Pierre Hubschman, Darvin Yi
Purpose of review: Technologies in healthcare incorporating artificial intelligence tools are experiencing rapid growth in static-image-based applications such as diagnostic imaging. Given the proliferation of artificial intelligence (AI)-technologies created for video-based imaging, ophthalmic microsurgery is likely to experience significant benefits from the application of emerging technologies to multiple facets of the care of the surgical patient.
Recent findings: Proof-of-concept research and early phase clinical trials are in progress for AI-based surgical technologies that aim to provide preoperative planning and decision support, intraoperative image enhancement, surgical guidance, surgical decision-making support, tactical assistive technologies, enhanced surgical training and assessment of trainee progress, and semi-autonomous tool control or autonomous elements of surgical procedures.
Summary: The proliferation of AI-based technologies in static imaging in clinical ophthalmology, continued refinement of AI tools designed for video-based applications, and development of AI-based digital tools in allied surgical fields suggest that ophthalmic surgery is poised for the integration of AI into our microsurgical paradigm.
{"title":"Artificial intelligence applications in ophthalmic surgery.","authors":"Yannek I Leiderman, Matthew J Gerber, Jean-Pierre Hubschman, Darvin Yi","doi":"10.1097/ICU.0000000000001033","DOIUrl":"https://doi.org/10.1097/ICU.0000000000001033","url":null,"abstract":"<p><strong>Purpose of review: </strong>Technologies in healthcare incorporating artificial intelligence tools are experiencing rapid growth in static-image-based applications such as diagnostic imaging. Given the proliferation of artificial intelligence (AI)-technologies created for video-based imaging, ophthalmic microsurgery is likely to experience significant benefits from the application of emerging technologies to multiple facets of the care of the surgical patient.</p><p><strong>Recent findings: </strong>Proof-of-concept research and early phase clinical trials are in progress for AI-based surgical technologies that aim to provide preoperative planning and decision support, intraoperative image enhancement, surgical guidance, surgical decision-making support, tactical assistive technologies, enhanced surgical training and assessment of trainee progress, and semi-autonomous tool control or autonomous elements of surgical procedures.</p><p><strong>Summary: </strong>The proliferation of AI-based technologies in static imaging in clinical ophthalmology, continued refinement of AI tools designed for video-based applications, and development of AI-based digital tools in allied surgical fields suggest that ophthalmic surgery is poised for the integration of AI into our microsurgical paradigm.</p>","PeriodicalId":50604,"journal":{"name":"Current Opinion in Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141983791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Purpose of review: To review the pathophysiology, recent biomarkers related to the ocular aspects of Steven-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN), and to highlight notable evidence published in recent years.
Recent findings: Several studies reveal the relationship between tear cytokines and the pathological components in eyes of SJS/TEN patients. Specific clinical features and associated risk factors in the acute stage have shown significant correlations with chronic ocular sequelae. Recent treatment protocols, including early pulse systemic and topical steroids, as well as tumor necrosis factor-α inhibitors, have demonstrated positive effects on ocular outcomes. In addition to conventional surgical treatment, a new surgical technique, simple oral mucosal epithelial transplantation (SOMET), has been introduced as a simple ocular surface reconstruction for patient with SJS.
Summary: Advancements in knowledge and management strategies have notably enhanced ocular outcomes for SJS/TEN eyes. A deeper understanding of the biomarker changes in these eyes could facilitate the development of future targeted treatment options.
{"title":"Ocular involvement in Steven-Johnson syndrome/toxic epidermal necrolysis: recent insights into pathophysiology, biomarkers, and therapeutic strategies.","authors":"Punyanuch Pisitpayat, Sarayut Nijvipakul, Passara Jongkhajornpong","doi":"10.1097/ICU.0000000000001079","DOIUrl":"https://doi.org/10.1097/ICU.0000000000001079","url":null,"abstract":"<p><strong>Purpose of review: </strong>To review the pathophysiology, recent biomarkers related to the ocular aspects of Steven-Johnson syndrome (SJS)/toxic epidermal necrolysis (TEN), and to highlight notable evidence published in recent years.</p><p><strong>Recent findings: </strong>Several studies reveal the relationship between tear cytokines and the pathological components in eyes of SJS/TEN patients. Specific clinical features and associated risk factors in the acute stage have shown significant correlations with chronic ocular sequelae. Recent treatment protocols, including early pulse systemic and topical steroids, as well as tumor necrosis factor-α inhibitors, have demonstrated positive effects on ocular outcomes. In addition to conventional surgical treatment, a new surgical technique, simple oral mucosal epithelial transplantation (SOMET), has been introduced as a simple ocular surface reconstruction for patient with SJS.</p><p><strong>Summary: </strong>Advancements in knowledge and management strategies have notably enhanced ocular outcomes for SJS/TEN eyes. A deeper understanding of the biomarker changes in these eyes could facilitate the development of future targeted treatment options.</p>","PeriodicalId":50604,"journal":{"name":"Current Opinion in Ophthalmology","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141972278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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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