Subthreshold Exudative Choroidal Neovascularization (CNV): Presentation of This Uncommon Subtype and Other CNVs in Age-Related Macular Degeneration (AMD)

Douglas, Vivian Paraskevi; Garg, Itika; Douglas, Konstantinos A A; Miller, John B.

doi:10.3390/jcm11082083

Cited by 3 publications

(1 citation statement)

References 47 publications

(69 reference statements)

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“…6 quantitative symptoms based on OCT are concluded as the macular epiretinal membrane (MEM) (13), retinal fluid accumulation (14), subretinal accumulation (15), subretinal hyperreflective material (SHRM) (16), vitreous warts (drusen) (17) and retinal detachment epithelium (18). When it comes to FAF, RCFP, and UWF, 13 potential symptoms may be able to be discovered, which are vitreous membrane in the macular region (19); 2) focal hyperpigmentation (20), MEM (13),choroidal neovascularization (CNV) in macular area (21,22), complete circular scotoma (completely black ring circle) (23), retinal pigment epithelial(RPE) detachment (24), subthreshold exudative choroidal neovascularization (25), sub-foveal intraretinal hemorrhage (26), large choroidal vessels and deep capillary plexus vessel in the macular region and glial scar tissue in the perimacular area (4,27), hemorrhagic spots and vitreous debris in the macular area (28), subretinal hemorrhage with residue in the perimacular region (1,4), glial scar in the macular region (29) and temporal margin hemorrhage residue in the macular region (1,4,30). However, it is time-consuming for ophthalmologists to detect AMD manually.…”

Section: Introductionmentioning

confidence: 99%

A Fusion Learning Method for the Age-Related Macular Degeneration Diagnosis Based on Multiple Sources of Ophthalmic Digital Images

Wang¹,

Zhou²,

Tang³

et al. 2022

Preprint

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Age-related Macular Degeneration (AMD) is one of the most causes for elders’ vision loss, early screening and treatment are the most efficient way to reduce the rate of blindness. AI-based methods based on ophthalmic images play a gat potential for AMD diagnosis. However, the difficulty of computing device obtaining, multiple evidence of image sources, time-wasting, and low level of explanation are challenges for AI models applicated in clinics. Thus, this study proposed a fusion learning method for AMD detection. Three steps are involved, which are image feature extraction, feature matrix fusion, and MLP-based AMD classification. Unsupervised (Hierarchical Clustering, SVM, and ResNet-K Means), supervised (VGG-16 and ResNet) methods and the proposed method are compared based on Optical Coherence Tomography (OCT), Fundus Autofluorescence (FAF), regular fundus photography (RCFP) and Ultra-Widefield Fundus (UWF), respectively and comprehensively. Findings show that the proposed method presents a high performance for integrated ophthalmic image diagnosis, it is timesaving (0.09s per image) with high precision (0.95), sensitivity (0.93), specificity (0.92) and AUC (0.94). Thus, this study concluded that the proposed method is a solution to AMD automatic quick detecting based on multiple data sources. A real-world UWF database is involved from Shenzhen Aier Hospital. Practical and theoretical contributions are delivered. A reference value for medical diagnosis based on multiple digital images is contributed.

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Section: Introductionmentioning

confidence: 99%

A Fusion Learning Method for the Age-Related Macular Degeneration Diagnosis Based on Multiple Sources of Ophthalmic Digital Images

Wang¹,

Zhou²,

Tang³

et al. 2022

Preprint

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Fusion learning methods for the age-related macular degeneration diagnosis based on multiple sources of ophthalmic digital images

Wang,

Zhou,

Huang

et al. 2024

Second International Conference on Electrical, Electronics, and Information Engineering (EEIE 2023)

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Age-related Macular Degeneration (AMD) is one of the major causes of elders' vision losses, and therefore its early screening and treatment are the most efficient way to reduce the risk of blindness. AI-based methods based on ophthalmic images have great potential for AMD diagnosis. However, low levels of accuracy, robustness, and explainability are challenges for AI approaches to be clinically applied. Traditionally unsupervised methods (Hierarchical Clustering and K-Means) and supervised methods (SVM, VGG-16, and ResNet), are used for AI-based AMD detection using different image datasets. However, single data sources and single models are not able to reflect the real data distribution, thus leading to low accuracy and robustness. Thus, this study proposes a multi-data source fusion method and a multi-model fusion approach for detecting AMD. Based on Optical Coherence Tomography (OCT), Fundus Autofluorescence (FAF), regular color fundus photography (CFP), and Ultra-Wide field Fundus (UWF) images, the multi-data source fusion method preprocesses and enhances each type of data, extracts features using unsupervised ML models, combines and normalizes them, and learns a model using a multi-layer perception (MLP) algorithm. The multi-model fusion method builds the model using different supervised machine learning and deep learning algorithms and adopts a voting mechanism for the model selection and optimization. Findings show that the proposed methods achieve higher accuracy and robustness than the traditional methods.

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Progressive Choriocapillaris Changes on Optical Coherence Tomography Angiography Correlate With Stage Progression in AMD

Romano,

Ding,

Yuan

et al. 2024

Invest. Ophthalmol. Vis. Sci.

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Subthreshold Exudative Choroidal Neovascularization (CNV): Presentation of This Uncommon Subtype and Other CNVs in Age-Related Macular Degeneration (AMD)

Cited by 3 publications

References 47 publications

A Fusion Learning Method for the Age-Related Macular Degeneration Diagnosis Based on Multiple Sources of Ophthalmic Digital Images

A Fusion Learning Method for the Age-Related Macular Degeneration Diagnosis Based on Multiple Sources of Ophthalmic Digital Images

Fusion learning methods for the age-related macular degeneration diagnosis based on multiple sources of ophthalmic digital images

Progressive Choriocapillaris Changes on Optical Coherence Tomography Angiography Correlate With Stage Progression in AMD

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