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Editorial introductions
doi : 10.1097/ICU.0000000000000792
September 2021 - Volume 32 - Issue 5 - p v-vi
Artificial intelligence-based predictions in neovascular age-related macular degeneration
Ferrara, Danielaa; Newton, Elizabeth M.a; Lee, Aaron Y.b
doi : 10.1097/ICU.0000000000000782
September 2021 - Volume 32 - Issue 5 - p 389-396
Predicting treatment response and optimizing treatment regimen in patients with neovascular age-related macular degeneration (nAMD) remains challenging. Artificial intelligence-based tools have the potential to increase confidence in clinical development of new therapeutics, facilitate individual prognostic predictions, and ultimately inform treatment decisions in clinical practice.
Deep learning-based natural language processing in ophthalmology: applications, challenges and future directions
Yang, Lily Wei Yuna,?; Ng, Wei Yanb,c,?; Foo, Li Lianb,c; Liu, Yongd; Yan, Mingd; Lei, Xiaofengd; Zhang, Xiaomand; Ting, Daniel Shu Weib,c
doi : 10.1097/ICU.0000000000000789
September 2021 - Volume 32 - Issue 5 - p 397-405
Artificial intelligence (AI) is the fourth industrial revolution in mankind's history. Natural language processing (NLP) is a type of AI that transforms human language, to one that computers can interpret and process. NLP is still in the formative stages of development in healthcare, with promising applications and potential challenges in its applications. This review provides an overview of AI-based NLP, its applications in healthcare and ophthalmology, next-generation use case, as well as potential challenges in deployment.
Automated deep learning in ophthalmology: AI that can build AI
O’Byrne, Ciaraa,b; Abbas, Abdallaha,c; Korot, Edwarda,d; Keane, Pearse A.a,e
doi : 10.1097/ICU.0000000000000779
September 2021 - Volume 32 - Issue 5 - p 406-412
The purpose of this review is to describe the current status of automated deep learning in healthcare and to explore and detail the development of these models using commercially available platforms. We highlight key studies demonstrating the effectiveness of this technique and discuss current challenges and future directions of automated deep learning.
Artificial intelligence in myopia: current and future trends
Foo, Li Liana,b; Ng, Wei Yana,b; Lim, Gilbert Yong Sana; Tan, Tien-Ena; Ang, Marcusa,b; Ting, Daniel Shu Weia,b
doi : 10.1097/ICU.0000000000000791
September 2021 - Volume 32 - Issue 5 - p 413-424
Myopia is one of the leading causes of visual impairment, with a projected increase in prevalence globally. One potential approach to address myopia and its complications is early detection and treatment. However, current healthcare systems may not be able to cope with the growing burden. Digital technological solutions such as artificial intelligence (AI) have emerged as a potential adjunct for myopia management.
Artificial intelligence and ophthalmic surgery
Mishra, Kapil; Leng, Theodore
doi : 10.1097/ICU.0000000000000788
September 2021 - Volume 32 - Issue 5 - p 425-430
Artificial intelligence and deep learning have become important tools in extracting data from ophthalmic surgery to evaluate, teach, and aid the surgeon in all phases of surgical management. The purpose of this review is to highlight the ever-increasing intersection of computer vision, machine learning, and ophthalmic microsurgery.
Gaps in standards for integrating artificial intelligence technologies into ophthalmic practice
Baxter, Sally L.a,b; Lee, Aaron Y.c
doi : 10.1097/ICU.0000000000000781
September 2021 - Volume 32 - Issue 5 - p 431-438
The purpose of this review is to provide an overview of healthcare standards and their relevance to multiple ophthalmic workflows, with a specific emphasis on describing gaps in standards development needed for improved integration of artificial intelligence technologies into ophthalmic practice.
Systemic retinal biomarkers
Ranchod, Tushar M.
doi : 10.1097/ICU.0000000000000784
September 2021 - Volume 32 - Issue 5 - p 439-444
Systemic retinal biomarkers are biomarkers identified in the retina and related to evaluation and management of systemic disease. This review summarizes the background, categories and key findings from this body of research as well as potential applications to clinical care.
Clinician-driven artificial intelligence in ophthalmology: resources enabling democratization
Korot, Edwarda,b; Gonçalves, Mariana B.b,c,d; Khan, Saad M.e; Struyven, Robbertb,f; Wagner, Siegfried K.b; Keane, Pearse A.b
doi : 10.1097/ICU.0000000000000785
September 2021 - Volume 32 - Issue 5 - p 445-451
This article aims to discuss the current state of resources enabling the democratization of artificial intelligence (AI) in ophthalmology
Applications of interpretability in deep learning models for ophthalmology
Hanif, Adam M.a,?; Beqiri, Sarab,?; Keane, Pearse A.c,d; Campbell, J. Petera
doi : 10.1097/ICU.0000000000000780
September 2021 - Volume 32 - Issue 5 - p 452-458
In this article, we introduce the concept of model interpretability, review its applications in deep learning models for clinical ophthalmology, and discuss its role in the integration of artificial intelligence in healthcare.
Generative adversarial networks in ophthalmology: what are these and how can they be used?
Wang, Zhaorana,?; Lim, Gilberta,b,?; Ng, Wei Yana,b; Keane, Pearse A.c; Campbell, J. Peterd; Tan, Gavin Siew Weia,b; Schmetterer, Leopolda,b,e,f,g,h,i; Wong, Tien Yina,b; Liu, Yongc; Ting, Daniel Shu Weia,b
doi : 10.1097/ICU.0000000000000794
September 2021 - Volume 32 - Issue 5 - p 459-467
The development of deep learning (DL) systems requires a large amount of data, which may be limited by costs, protection of patient information and low prevalence of some conditions. Recent developments in artificial intelligence techniques have provided an innovative alternative to this challenge via the synthesis of biomedical images within a DL framework known as generative adversarial networks (GANs). This paper aims to introduce how GANs can be deployed for image synthesis in ophthalmology and to discuss the potential applications of GANs-produced images.
Complications of retinopathy of prematurity treatment
Barnett, Joshua M.; Hubbard, G. Baker
doi : 10.1097/ICU.0000000000000783
September 2021 - Volume 32 - Issue 5 - p 475-481
The purpose of this review is to summarize complications of treatment for retinopathy of prematurity (ROP) and to compare complications of laser and intravitreal antivascular endothelial growth factor (VEGF) injections.
Surgery for stage 5 retinopathy of prematurity
Mart?nez-Castellanos, Mar?a Ana; Ortiz-Ramirez, Greacia Yael
doi : 10.1097/ICU.0000000000000795
September 2021 - Volume 32 - Issue 5 - p 482-488
Our understanding of the pathogenesis and surgical management of stage 5 retinopathy of prematurity has come a long way. Despite of new technologies in retinal surgical devices, the dissection of thick membranes is still a challenge. We use a capsulotomy ‘plug on tip’ 0.05 mm designed for capsular fimosis. This diathermy instrument is used to cut the lens capsule by low power waves transmitted from the tip of an active incising electrode and make incisions in the tissue. We tested this technique with 226 infants of which all 226 eyes retrolental membrane were removed. In 6–46 months follow-up, light perception or better visual function was achieved in 92%.
Adult retinopathy of prematurity: treatment implications, long term sequelae, and management
Chang, Emmanuela,b; Rao, Prethya,b
doi : 10.1097/ICU.0000000000000787
September 2021 - Volume 32 - Issue 5 - p 489-493
Classically, ROP has been considered a neonatal disease only; however, pediatric ophthalmologists and retinal specialists worldwide are recently facing a new paradigm shift. retinopathy of prematurity (ROP) is now considered a lifelong disease that extends well into adulthood. The purpose of this review is to describe the adult ROP anatomy and discuss the late sequelae and management of this disease.
ROP Risk Management 2021
Gold, Robert S.
doi : 10.1097/ICU.0000000000000790
September 2021 - Volume 32 - Issue 5 - p 494-497
It is important for ophthalmologists to keep current with up-to-date recommendations for screening, treating, and follow-up of infants with retinopathy of prematurity (ROP). This paper will review updated ROP Safety Net protocols and Policy Statements to stress that following risk management principles can avoid claims that could arise from poor visual outcomes.
Description and management of retinopathy of prematurity reactivation after intravitreal antivascular endothelial growth factor therapy
Valikodath, Nita G.a; Chiang, Michael F.b; Chan, R.V. Paula
doi : 10.1097/ICU.0000000000000786
September 2021 - Volume 32 - Issue 5 - p 468-474
To review the literature regarding reactivation of retinopathy of prematurity (ROP) after treatment with antivascular endothelial growth factor (anti-VEGF) agents.
