Artificial Intelligence and Optical Coherence Tomography Imaging.

This review article aimed to highlight the application and use of artificial intelligence (AI) in optical coherence tomography (OCT) imaging in ophthalmology. Artificial intelligence programs seek to simulate intelligent human behavior in computers. With an abundance of patient data, especially with the advent and growing use of imaging modalities such as OCT, AI programs provide us with the unique opportunity to analyze this plethora of information and assist in making clinical decisions in the field of ophthalmology. Groups around the world have developed and evaluated AI programs that gather data from diagnostic modalities, such as OCT, that assist in the diagnosis and management of ophthalmological diseases with a high accuracy. Artificial intelligence programs using OCT have the potential to play a significant role in the diagnosis and management of ophthalmological disease in the near future. Incorporation of AI in medicine, however, is not without its pitfalls. Some limitations of AI in ophthalmology are also discussed in this review. These include the deskilling of physicians due to increase in reliance on automation, inability of AI programs to take a holistic approach to clinical encounters with patients, requirement of pre-existing strong datasets to train AI programs, and the inability of AI programs to incorporate the ambiguity and variability that is intrinsic to the nature of clinical medicine.

Kapoor R ，Whigham BT ，Al-Aswad LA 《Asia-Pacific Journal of Ophthalmology》

Ophthalmology Optical Coherence Tomography Databases for Artificial Intelligence Algorithm: A Review.

Restrepo D ，Quion JM ，Do Carmo Novaes F ，Azevedo Costa ID ，Vasquez C ，Bautista AN ，Quiminiano E ，Lim PA ，Mwavu R ，Celi LA ，Nakayama LF ... -  《-》

Artificial Intelligence (AI) and Retinal Optical Coherence Tomography (OCT).

Dahrouj M ，Miller JB 《-》

Deep learning in ophthalmology: The technical and clinical considerations.

The advent of computer graphic processing units, improvement in mathematical models and availability of big data has allowed artificial intelligence (AI) using machine learning (ML) and deep learning (DL) techniques to achieve robust performance for broad applications in social-media, the internet of things, the automotive industry and healthcare. DL systems in particular provide improved capability in image, speech and motion recognition as well as in natural language processing. In medicine, significant progress of AI and DL systems has been demonstrated in image-centric specialties such as radiology, dermatology, pathology and ophthalmology. New studies, including pre-registered prospective clinical trials, have shown DL systems are accurate and effective in detecting diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), retinopathy of prematurity, refractive error and in identifying cardiovascular risk factors and diseases, from digital fundus photographs. There is also increasing attention on the use of AI and DL systems in identifying disease features, progression and treatment response for retinal diseases such as neovascular AMD and diabetic macular edema using optical coherence tomography (OCT). Additionally, the application of ML to visual fields may be useful in detecting glaucoma progression. There are limited studies that incorporate clinical data including electronic health records, in AL and DL algorithms, and no prospective studies to demonstrate that AI and DL algorithms can predict the development of clinical eye disease. This article describes global eye disease burden, unmet needs and common conditions of public health importance for which AI and DL systems may be applicable. Technical and clinical aspects to build a DL system to address those needs, and the potential challenges for clinical adoption are discussed. AI, ML and DL will likely play a crucial role in clinical ophthalmology practice, with implications for screening, diagnosis and follow up of the major causes of vision impairment in the setting of ageing populations globally.

Ting DSW ，Peng L ，Varadarajan AV ，Keane PA ，Burlina PM ，Chiang MF ，Schmetterer L ，Pasquale LR ，Bressler NM ，Webster DR ，Abramoff M ，Wong TY ... -  《-》

An overview of artificial intelligence in diabetic retinopathy and other ocular diseases.

Artificial intelligence (AI), also known as machine intelligence, is a branch of science that empowers machines using human intelligence. AI refers to the technology of rendering human intelligence through computer programs. From healthcare to the precise prevention, diagnosis, and management of diseases, AI is progressing rapidly in various interdisciplinary fields, including ophthalmology. Ophthalmology is at the forefront of AI in medicine because the diagnosis of ocular diseases heavy reliance on imaging. Recently, deep learning-based AI screening and prediction models have been applied to the most common visual impairment and blindness diseases, including glaucoma, cataract, age-related macular degeneration (ARMD), and diabetic retinopathy (DR). The success of AI in medicine is primarily attributed to the development of deep learning algorithms, which are computational models composed of multiple layers of simulated neurons. These models can learn the representations of data at multiple levels of abstraction. The Inception-v3 algorithm and transfer learning concept have been applied in DR and ARMD to reuse fundus image features learned from natural images (non-medical images) to train an AI system with a fraction of the commonly used training data (<1%). The trained AI system achieved performance comparable to that of human experts in classifying ARMD and diabetic macular edema on optical coherence tomography images. In this study, we highlight the fundamental concepts of AI and its application in these four major ocular diseases and further discuss the current challenges, as well as the prospects in ophthalmology.

Sheng B ，Chen X ，Li T ，Ma T ，Yang Y ，Bi L ，Zhang X ... -  《Frontiers in Public Health》