Deep learning–based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis
Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images. The performances of these algorithms vary and often c...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wolters Kluwer Medknow Publications
2023-01-01
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| Series: | Indian Journal of Ophthalmology |
| Subjects: | |
| Online Access: | http://www.ijo.in/article.asp?issn=0301-4738;year=2023;volume=71;issue=5;spage=1783;epage=1796;aulast=Manikandan |
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| author | Suchetha Manikandan Rajiv Raman Ramachandran Rajalakshmi S Tamilselvi R Janani Surya |
| author_facet | Suchetha Manikandan Rajiv Raman Ramachandran Rajalakshmi S Tamilselvi R Janani Surya |
| author_sort | Suchetha Manikandan |
| collection | DOAJ |
| description | Diabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images. The performances of these algorithms vary and often create doubt regarding their clinical utility. In resource-constrained health-care systems, these algorithms may play an important role in determining referral and treatment. The survey provides a diversified overview of macular edema detection methods, including cutting-edge research, with the objective of providing pertinent information to research groups, health-care professionals, and diabetic patients about the applications of deep learning in retinal image detection and classification process. Electronic databases such as PubMed, IEEE Explore, BioMed, and Google Scholar were searched from inception to March 31, 2022, and the reference lists of published papers were also searched. The study followed the preferred reporting items for systematic review and meta-analysis (PRISMA) reporting guidelines. Examination of various deep learning models and their exhibition regarding precision, epochs, their capacity to detect anomalies for less training data, concepts, and challenges that go deep into the applications were analyzed. A total of 53 studies were included that evaluated the performance of deep learning models in a total of 1,414,169°CT volumes, B-scans, patients, and 472,328 fundus images. The overall area under the receiver operating characteristic curve (AUROC) was 0.9727. The overall sensitivity for detecting DME using OCT images was 96% (95% confidence interval [CI]: 0.94–0.98). The overall sensitivity for detecting DME using fundus images was 94% (95% CI: 0.90–0.96). |
| first_indexed | 2024-03-12T22:31:39Z |
| format | Article |
| id | doaj.art-65541d0b134841278fa4c38cc248103c |
| institution | Directory Open Access Journal |
| issn | 0301-4738 1998-3689 |
| language | English |
| last_indexed | 2024-03-12T22:31:39Z |
| publishDate | 2023-01-01 |
| publisher | Wolters Kluwer Medknow Publications |
| record_format | Article |
| series | Indian Journal of Ophthalmology |
| spelling | doaj.art-65541d0b134841278fa4c38cc248103c2023-07-21T15:14:09ZengWolters Kluwer Medknow PublicationsIndian Journal of Ophthalmology0301-47381998-36892023-01-017151783179610.4103/IJO.IJO_2614_22Deep learning–based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysisSuchetha ManikandanRajiv RamanRamachandran RajalakshmiS TamilselviR Janani SuryaDiabetic macular edema (DME) is an important cause of visual impairment in the working-age group. Deep learning methods have been developed to detect DME from two-dimensional retinal images and also from optical coherence tomography (OCT) images. The performances of these algorithms vary and often create doubt regarding their clinical utility. In resource-constrained health-care systems, these algorithms may play an important role in determining referral and treatment. The survey provides a diversified overview of macular edema detection methods, including cutting-edge research, with the objective of providing pertinent information to research groups, health-care professionals, and diabetic patients about the applications of deep learning in retinal image detection and classification process. Electronic databases such as PubMed, IEEE Explore, BioMed, and Google Scholar were searched from inception to March 31, 2022, and the reference lists of published papers were also searched. The study followed the preferred reporting items for systematic review and meta-analysis (PRISMA) reporting guidelines. Examination of various deep learning models and their exhibition regarding precision, epochs, their capacity to detect anomalies for less training data, concepts, and challenges that go deep into the applications were analyzed. A total of 53 studies were included that evaluated the performance of deep learning models in a total of 1,414,169°CT volumes, B-scans, patients, and 472,328 fundus images. The overall area under the receiver operating characteristic curve (AUROC) was 0.9727. The overall sensitivity for detecting DME using OCT images was 96% (95% confidence interval [CI]: 0.94–0.98). The overall sensitivity for detecting DME using fundus images was 94% (95% CI: 0.90–0.96).http://www.ijo.in/article.asp?issn=0301-4738;year=2023;volume=71;issue=5;spage=1783;epage=1796;aulast=Manikandandeep learningdiabetic macular edemafundus imagesmeta-analysisoptical coherence tomography |
| spellingShingle | Suchetha Manikandan Rajiv Raman Ramachandran Rajalakshmi S Tamilselvi R Janani Surya Deep learning–based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis Indian Journal of Ophthalmology deep learning diabetic macular edema fundus images meta-analysis optical coherence tomography |
| title | Deep learning–based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis |
| title_full | Deep learning–based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis |
| title_fullStr | Deep learning–based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis |
| title_full_unstemmed | Deep learning–based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis |
| title_short | Deep learning–based detection of diabetic macular edema using optical coherence tomography and fundus images: A meta-analysis |
| title_sort | deep learning based detection of diabetic macular edema using optical coherence tomography and fundus images a meta analysis |
| topic | deep learning diabetic macular edema fundus images meta-analysis optical coherence tomography |
| url | http://www.ijo.in/article.asp?issn=0301-4738;year=2023;volume=71;issue=5;spage=1783;epage=1796;aulast=Manikandan |
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