The American Society of Retina Specialists Artificial Intelligence Task Force Report

2020 ◽  
Vol 4 (4) ◽  
pp. 312-319
Author(s):  
Katherine E. Talcott ◽  
Judy E. Kim ◽  
Yasha Modi ◽  
Darius M. Moshfeghi ◽  
Rishi P. Singh
Keyword(s):  
Artificial Intelligence ◽  
Practice Patterns ◽  
Task Force ◽  
Age Related Macular Degeneration ◽  
Evidence Based ◽  
Referral Patterns ◽  
Task Force Report ◽  
Age Related ◽  
Diagnostic Capability ◽  
American Society

Artificial intelligence (AI) is a growing area that relies on the heavy use of diagnostic imaging within the field of retina to offer exciting advancements in diagnostic capability to better understand and manage retinal conditions such as diabetic retinopathy, diabetic macular edema, age-related macular degeneration, and retinopathy of prematurity. However, there are discrepancies between the findings of these AI programs and their referral recommendations compared with evidence-based referral patterns, such as Preferred Practice Patterns by the American Academy of Ophthalmology. The overall focus of this task force report is to first describe the work in AI being completed in the management of retinal conditions. This report also discusses the guidelines of the Preferred Practice Pattern and how they can be used in the emerging field of AI.

scholarly journals Development of a Deep-Learning-Based Artificial Intelligence Tool for Differential Diagnosis between Dry and Neovascular Age-Related Macular Degeneration

Diagnostics ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 261
Author(s):  
Tae-Young Heo ◽  
Kyoung Min Kim ◽  
Hyun Kyu Min ◽  
Sun Mi Gu ◽  
Jae Hyun Kim ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Differential Diagnosis ◽  
Deep Learning ◽  
Macular Degeneration ◽  
Data Augmentation ◽  
Treatment Strategies ◽  
Image Data ◽  
Age Related Macular Degeneration ◽  
Age Related ◽  
Leave One Out

The use of deep-learning-based artificial intelligence (AI) is emerging in ophthalmology, with AI-mediated differential diagnosis of neovascular age-related macular degeneration (AMD) and dry AMD a promising methodology for precise treatment strategies and prognosis. Here, we developed deep learning algorithms and predicted diseases using 399 images of fundus. Based on feature extraction and classification with fully connected layers, we applied the Visual Geometry Group with 16 layers (VGG16) model of convolutional neural networks to classify new images. Image-data augmentation in our model was performed using Keras ImageDataGenerator, and the leave-one-out procedure was used for model cross-validation. The prediction and validation results obtained using the AI AMD diagnosis model showed relevant performance and suitability as well as better diagnostic accuracy than manual review by first-year residents. These results suggest the efficacy of this tool for early differential diagnosis of AMD in situations involving shortages of ophthalmology specialists and other medical devices.

Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology

2020 ◽  
pp. bjophthalmol-2019-315651 ◽  
Author(s):  
Darren Shu Jeng Ting ◽  
Valencia HX Foo ◽  
Lily Wei Yun Yang ◽  
Josh Tjunrong Sia ◽  
Marcus Ang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Refractive Surgery ◽  
Anterior Segment ◽  
Age Related Macular Degeneration ◽  
Angle Closure ◽  
Corneal Transplant ◽  
Future Directions ◽  
Age Related ◽  
Clinical Considerations ◽  
Intelligent Healthcare

With the advancement of computational power, refinement of learning algorithms and architectures, and availability of big data, artificial intelligence (AI) technology, particularly with machine learning and deep learning, is paving the way for ‘intelligent’ healthcare systems. AI-related research in ophthalmology previously focused on the screening and diagnosis of posterior segment diseases, particularly diabetic retinopathy, age-related macular degeneration and glaucoma. There is now emerging evidence demonstrating the application of AI to the diagnosis and management of a variety of anterior segment conditions. In this review, we provide an overview of AI applications to the anterior segment addressing keratoconus, infectious keratitis, refractive surgery, corneal transplant, adult and paediatric cataracts, angle-closure glaucoma and iris tumour, and highlight important clinical considerations for adoption of AI technologies, potential integration with telemedicine and future directions.

scholarly journals Artificial intelligence and deep learning in ophthalmology

2018 ◽  
Vol 103 (2) ◽  
pp. 167-175 ◽  
Author(s):  
Daniel Shu Wei Ting ◽  
Louis R Pasquale ◽  
Lily Peng ◽  
John Peter Campbell ◽  
Aaron Y Lee ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Language Processing ◽  
Visual Fields ◽  
Classification Performance ◽  
Age Related Macular Degeneration ◽  
Patient Acceptance ◽  
Age Related ◽  
Ocular Imaging ◽  
Fundus Photographs

Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward.

Applications of Artificial Intelligence for the Diagnosis, Prognosis, and Treatment of Age-related Macular Degeneration

2020 ◽  
Vol 60 (4) ◽  
pp. 147-168
Author(s):  
Louis Cai ◽  
John W. Hinkle ◽  
Diego Arias ◽  
Richard J. Gorniak ◽  
Paras C. Lakhani ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Macular Degeneration ◽  
Age Related Macular Degeneration ◽  
Age Related

scholarly journals American Society for Bone and Mineral Research‐Orthopaedic Research Society Joint Task Force Report on Cell‐Based Therapies

2019 ◽  
Vol 35 (1) ◽  
pp. 3-17 ◽  
Author(s):  
Regis J O'Keefe ◽  
Rocky S Tuan ◽  
Nancy E Lane ◽  
Hani A Awad ◽  
Frank Barry ◽  
...  
Keyword(s):  
Task Force ◽  
Research Society ◽  
Mineral Research ◽  
Task Force Report ◽  
Orthopaedic Research ◽  
Joint Task ◽  
Joint Task Force ◽  
American Society
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