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Detection of macular atrophy in age-related macular degeneration aided by artificial intelligence
Wei Wei1,2,3, Rajeevan Anantharanjit3,4, Radhika Pooja Patel3,4
1Department of Ophthalmology, Ningbo Medical Center Lihuili Hospital, Ningbo, China.
Introduction:
Age-related macular degeneration (AMD) is a leading cause of irreversible visual impairment worldwide. The endpoint of AMD, both in its dry or wet form, is macular atrophy (MA) which is characterized by the permanent loss of the RPE and overlying photoreceptors either in dry AMD or in wet AMD. A recognized unmet need in AMD is the early detection of MA development.
Areas Covered:
Artificial Intelligence (AI) has demonstrated great impact in detection of retinal diseases, especially with its robust ability to analyze big data afforded by ophthalmic imaging modalities, such as color fundus photography (CFP), fundus autofluorescence (FAF), near-infrared reflectance (NIR), and optical coherence tomography (OCT). Among these, OCT has been shown to have great promise in identifying early MA using the new criteria in 2018.
Expert Opinion:
There are few studies in which AI-OCT methods have been used to identify MA; however, results are very promising when compared to other imaging modalities. In this paper, we review the development and advances of ophthalmic imaging modalities and their combination with AI technology to detect MA in AMD. In addition, we emphasize the application of AI-OCT as an objective, cost-effective tool for the early detection and monitoring of the progression of MA in AMD.
Insights
Early detection of macular atrophy (MA) in age-related macular degeneration (AMD) is crucial. AI combined with optical coherence tomography (OCT) shows promise for objective, cost-effective MA identification and monitoring.
Area of Science:
- Ophthalmology
- Medical Imaging
- Artificial Intelligence
Background:
- Age-related macular degeneration (AMD) is a primary cause of irreversible vision loss globally.
- Macular atrophy (MA) represents the advanced stage of AMD, involving permanent loss of retinal pigment epithelium (RPE) and photoreceptors.
- Early detection of MA development in AMD remains a significant unmet clinical need.
Approach:
- Review of ophthalmic imaging modalities including color fundus photography (CFP), fundus autofluorescence (FAF), near-infrared reflectance (NIR), and optical coherence tomography (OCT).
- Focus on the integration of Artificial Intelligence (AI) with OCT for enhanced MA detection.
- Evaluation of AI-OCT methods for their potential in identifying early MA based on 2018 criteria.
Key Points:
- AI excels in analyzing large datasets from ophthalmic imaging, aiding in retinal disease detection.
- Optical coherence tomography (OCT) demonstrates significant potential for early MA identification.
- AI-OCT combinations are emerging as promising tools for MA detection, outperforming other modalities in preliminary studies.
Conclusions:
- AI-OCT offers an objective and cost-effective approach for the early detection of MA in AMD.
- This technology facilitates the monitoring of MA progression in patients with AMD.
- Further development and application of AI-OCT are essential for improving AMD patient outcomes.
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