Detection of Macular Neovascularization in Eyes Presenting with Macular Edema using OCT Angiography and a Deep Learning Model
Issued Date
2024-01-01
Resource Type
eISSN
24686530
Scopus ID
2-s2.0-85211707634
Pubmed ID
39461425
Journal Title
Ophthalmology Retina
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SCOPUS
Bibliographic Citation
Ophthalmology Retina (2024)
Suggested Citation
Wongchaisuwat N., Wang J., White E.S., Hwang T.S., Jia Y., Bailey S.T. Detection of Macular Neovascularization in Eyes Presenting with Macular Edema using OCT Angiography and a Deep Learning Model. Ophthalmology Retina (2024). doi:10.1016/j.oret.2024.10.017 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/102438
Title
Detection of Macular Neovascularization in Eyes Presenting with Macular Edema using OCT Angiography and a Deep Learning Model
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Abstract
Purpose: To test the diagnostic performance of an artificial intelligence algorithm for detecting and segmenting macular neovascularization (MNV) with OCT and OCT angiography (OCTA) in eyes with macular edema from various diagnoses. Design: Prospective cross-sectional study. Participants: Study participants with macular edema due to either treatment-naïve exudative age-related macular degeneration (AMD), diabetic macular edema (DME), or retinal vein occlusion (RVO). Methods: Study participants were imaged with macular 3 × 3–mm and 6 × 6–mm spectral-domain OCTA. Eyes with exudative AMD were required to have MNV in the central 3 × 3–mm area. A previously developed hybrid multitask convolutional neural network for MNV detection (aiMNV), and segmentation was applied to all images, regardless of image quality. Main Outcome Measures: Sensitivity, specificity, positive predictive value, and negative predictive value of detecting MNV and intersection over union (IoU) score and F1 score for segmentation. Results: Of 114 eyes from 112 study participants, 56 eyes had MNV due to exudative AMD and 58 eyes with macular edema due to either DME or RVO. The 3 × 3–mm OCTA scans with aiMNV detected MNV with 96.4% sensitivity, 98.3% specificity, 98.2% positive predictive value, and 96.6% negative predictive value. For segmentation, the average IoU score was 0.947, and the F1 score was 0.973. The 6 × 6–mm scans performed well; however, sensitivity for MNV detection was lower than 3 × 3–mm scans due to lower scan sampling density. Conclusions: This novel aiMNV algorithm can accurately detect and segment MNV in eyes with exudative AMD from a control group of eyes that present with macular edema from either DME or RVO. Higher scan sampling density improved the aiMNV sensitivity for MNV detection. Financial Disclosure(s): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.