An overview of artificial intelligence in diabetic retinopathy and other ocular diseases
Bibliographic Data
| ID | 22089933 |
|---|---|
| Authors | Bin Sheng (0000-0001-8678-2784, Beijing Tongren Hospital, corresponding author), Xiaosi Chen (0000-0003-0449-1700, Beijing Tongren Hospital), Tingyao Li (Beijing Tongren Hospital), Tianxing Ma (0000-0002-9887-4843, Chongqing University), Yang Yang (0000-0002-3705-7612, Beijing Tongren Hospital), Lei Bi (0000-0002-2951-0966, The University of Sydney), Xinyuan Zhang (0000-0002-6061-7884, Beijing Tongren Hospital, corresponding author) |
| Year | 2022 |
| Volume | 10 |
| Pages | 971943-971943 |
| Publication date | 2022-10-28 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2022.971943 |
| PMID | 36388304 |
| OpenAlex | W4307704705 |
| Language | EN |
| Citations received | 5 |
| References cited | 82 |
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
Deep learning · Diabetes mellitus · Diabetic retinopathy · Glaucoma · Machine learning · Macular degeneration · Medical imaging · Optical coherence tomography · Computer Science · Glaucoma and retinal disorders · Medicine · Retinal Diseases and Treatments · Retinal Imaging and Analysis · Artificial Intelligence · Ophthalmology · Optometry
Leveraging XGBoost and explainable AI for accurate prediction of type 2 diabetes
Desafios bioéticos do uso da inteligência artificial na oftalmologia
Desafíos bioéticos para el uso de la inteligencia artificial en oftalmología
Bioethical challenges in the use of artificial intelligence in ophthalmology
Exploring the mechanism of Bushen Huoxue prescription in the treatment of early diabetic retinal edema from the perspective of inner blood-retinal barrier injury
| Unique citing works | 5 |
|---|---|
| Citations per year | 2,5 |
| Citation span | 2024 - 2025 (2) |
| Citation velocity | recent |
| Highly cited | No |
| Citation types | Neutral: 5 |