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Artificial intelligence for diabetic retinopathy screening: a review
Open accessAbstract
Diabetes is a global eye health issue. Given the rising in diabetes prevalence and ageing population, this poses significant challenge to perform diabetic retinopathy (DR) screening for these patients. Artificial intelligence (AI) using machine learning and deep learning have been adopted by various groups to develop automated DR detection algorithms. This article aims to describe the state-of-art AI DR screening technologies that have been described in the literature, some of which are already commercially available. All these technologies were designed using different training datasets and technical methodologies. Although many groups have published robust diagnostic performance of the AI algorithms for DR screening, future research is required to address several challenges, for examples medicolegal implications, ethics, and clinical deployment model in order to expedite the translation of these novel technologies into the healthcare setting.
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“Artificial intelligence (AI) for medical diagnosis holds potential, demonstrating high performance in detecting various diseases.57”
From Using artificial intelligence to improve human performance: efficient retinal disease detection training with synthetic images (2024) · cites this paper as [7]
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Cited by
Sorted by citations
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“In the past, identifying DR relied solely on human clinicians or graders [67].”
BackgroundArtificial Intelligence: The Future for Diabetes Care
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“47 48 Beyond conventional screening programmes, the use of deep learning in medicine is increasing, and has been considered in the diagnosis of melanoma, 49 ophthalmic diseases (age-related macular degeneration 50 and glaucoma 51 ), and in interpretation of histological, 52 radiological, 53 and…”
BackgroundDeep learning for diabetic retinopathy detection and classification based on fundus images: A review
Tsiknakis, N., Theodoropoulos, D., Manikis, G. C., Ktistakis, E., Boutsora, O., Bertó, A., Scarpa, F., Scarpa, A., Fotiadis, D. I., & Kostas Marias, A. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104599 · Cited by 359
“Although some review articles regarding the application of deep learning methods on DR have been published during the past few years [18 – 22], most of them focus only on specific aspects of the data analysis and modeling pipeline, as is presented in Fig.”
Background
Metadata
- Authors
- Grzybowski, A., Brona, P., Lim, G., Ruamviboonsuk, P., Tan, G. S. W., Abramoff, M., & Ting, D. S. W.
- Affiliations
- Foundation for the Development of Ophthalmology (PL)
- University of Warmia and Mazury in Olsztyn (PL)
- National University of Singapore (SG)
- Rajavithi Hospital (TH)
- Corresponding author
- Ting, Daniel S. W.
- ORCID
- Venue
- Eye
- Published
- 2019-09-05
- Volume
- 34
- Issue
- 3
- Pages
- 451-460
- ISSN
- 0078-5334 (ISSN-L)
- 0950-222X
- 1476-5454
- PMC
- PMC7055592
- OpenAlex
- W2972246881
- Cited by
- 376
- References
- 36
- Citations by year
- 2026: 34 · 2025: 69 · 2024: 70 · 2023: 76 · 2022: 63 · 2021: 40 · 2020: 21 · 2019: 2
- FWCI
- 4.72
- Type
- journal article
- Language
- en
- Update notices
- correction (2019-12-10)
- Open access
- bronze
- doi.org
- Locations
- Eye (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
- Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Keywords
- diabetic retinopathy, retinopathy screening, artificial intelligence, automated detection algorithm, deep learning, machine learning, medicolegal issues, ethics, clinical deployment
- MeSH terms
- Machine Learning, Algorithms, Artificial Intelligence, Diabetes Mellitus, Diabetic Retinopathy, Humans, Mass Screening
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