Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions
Optical coherence tomography angiography (OCT-A) provides depth-resolved visualization of the retinal microvasculature without intravenous dye injection. It facilitates investigations of various retinal vascular diseases and glaucoma by assessment of qualitative and quantitative microvascular change...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/2/326 |
| _version_ | 1797443732433272832 |
|---|---|
| author | Dawei Yang An Ran Ran Truong X. Nguyen Timothy P. H. Lin Hao Chen Timothy Y. Y. Lai Clement C. Tham Carol Y. Cheung |
| author_facet | Dawei Yang An Ran Ran Truong X. Nguyen Timothy P. H. Lin Hao Chen Timothy Y. Y. Lai Clement C. Tham Carol Y. Cheung |
| author_sort | Dawei Yang |
| collection | DOAJ |
| description | Optical coherence tomography angiography (OCT-A) provides depth-resolved visualization of the retinal microvasculature without intravenous dye injection. It facilitates investigations of various retinal vascular diseases and glaucoma by assessment of qualitative and quantitative microvascular changes in the different retinal layers and radial peripapillary layer non-invasively, individually, and efficiently. Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has been applied in OCT-A image analysis in recent years and achieved good performance for different tasks, such as image quality control, segmentation, and classification. DL technologies have further facilitated the potential implementation of OCT-A in eye clinics in an automated and efficient manner and enhanced its clinical values for detecting and evaluating various vascular retinopathies. Nevertheless, the deployment of this combination in real-world clinics is still in the “proof-of-concept” stage due to several limitations, such as small training sample size, lack of standardized data preprocessing, insufficient testing in external datasets, and absence of standardized results interpretation. In this review, we introduce the existing applications of DL in OCT-A, summarize the potential challenges of the clinical deployment, and discuss future research directions. |
| first_indexed | 2024-03-09T13:02:16Z |
| format | Article |
| id | doaj.art-ef51254cecdb421c9d61c20bed4742d3 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-09T13:02:16Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-ef51254cecdb421c9d61c20bed4742d32023-11-30T21:53:56ZengMDPI AGDiagnostics2075-44182023-01-0113232610.3390/diagnostics13020326Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future DirectionsDawei Yang0An Ran Ran1Truong X. Nguyen2Timothy P. H. Lin3Hao Chen4Timothy Y. Y. Lai5Clement C. Tham6Carol Y. Cheung7Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, ChinaDepartment of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, ChinaDepartment of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, ChinaDepartment of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, ChinaDepartment of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong SAR, ChinaDepartment of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, ChinaDepartment of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, ChinaDepartment of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, ChinaOptical coherence tomography angiography (OCT-A) provides depth-resolved visualization of the retinal microvasculature without intravenous dye injection. It facilitates investigations of various retinal vascular diseases and glaucoma by assessment of qualitative and quantitative microvascular changes in the different retinal layers and radial peripapillary layer non-invasively, individually, and efficiently. Deep learning (DL), a subset of artificial intelligence (AI) based on deep neural networks, has been applied in OCT-A image analysis in recent years and achieved good performance for different tasks, such as image quality control, segmentation, and classification. DL technologies have further facilitated the potential implementation of OCT-A in eye clinics in an automated and efficient manner and enhanced its clinical values for detecting and evaluating various vascular retinopathies. Nevertheless, the deployment of this combination in real-world clinics is still in the “proof-of-concept” stage due to several limitations, such as small training sample size, lack of standardized data preprocessing, insufficient testing in external datasets, and absence of standardized results interpretation. In this review, we introduce the existing applications of DL in OCT-A, summarize the potential challenges of the clinical deployment, and discuss future research directions.https://www.mdpi.com/2075-4418/13/2/326optical coherence tomography angiographyimage qualityartificial intelligencedeep learningmedical image analysisdiabetic macular ischemia |
| spellingShingle | Dawei Yang An Ran Ran Truong X. Nguyen Timothy P. H. Lin Hao Chen Timothy Y. Y. Lai Clement C. Tham Carol Y. Cheung Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions Diagnostics optical coherence tomography angiography image quality artificial intelligence deep learning medical image analysis diabetic macular ischemia |
| title | Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions |
| title_full | Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions |
| title_fullStr | Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions |
| title_full_unstemmed | Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions |
| title_short | Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions |
| title_sort | deep learning in optical coherence tomography angiography current progress challenges and future directions |
| topic | optical coherence tomography angiography image quality artificial intelligence deep learning medical image analysis diabetic macular ischemia |
| url | https://www.mdpi.com/2075-4418/13/2/326 |
| work_keys_str_mv | AT daweiyang deeplearninginopticalcoherencetomographyangiographycurrentprogresschallengesandfuturedirections AT anranran deeplearninginopticalcoherencetomographyangiographycurrentprogresschallengesandfuturedirections AT truongxnguyen deeplearninginopticalcoherencetomographyangiographycurrentprogresschallengesandfuturedirections AT timothyphlin deeplearninginopticalcoherencetomographyangiographycurrentprogresschallengesandfuturedirections AT haochen deeplearninginopticalcoherencetomographyangiographycurrentprogresschallengesandfuturedirections AT timothyyylai deeplearninginopticalcoherencetomographyangiographycurrentprogresschallengesandfuturedirections AT clementctham deeplearninginopticalcoherencetomographyangiographycurrentprogresschallengesandfuturedirections AT carolycheung deeplearninginopticalcoherencetomographyangiographycurrentprogresschallengesandfuturedirections |