Exudative Progression of Treatment-Naïve Nonexudative Macular Neovascularization in Age-Related Macular Degeneration: A Systematic Review With Meta-Analyses

Nissen, Anne Helene Køllund; Kiilgaard, Hans Christian; van Dijk, Elon H.C.; Hajari, Javad Nouri; Huemer, Josef; Iovino, Claudio; Schneider, Miklos; Sørensen, Torben Lykke; Grauslund, Jakob; Subhi, Yousif

doi:10.1016/j.ajo.2023.08.020

American Journal of Ophthalmology

2024

DOI: 10.1016/j.ajo.2023.08.020

|View full text |Cite

Exudative Progression of Treatment-Naïve Nonexudative Macular Neovascularization in Age-Related Macular Degeneration: A Systematic Review With Meta-Analyses

Anne Helene Køllund Nissen,

Hans Christian Kiilgaard,

Elon H.C. van Dijk

et al.

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article2

Relationship

Self Cite0

Independent2

Authors

Journals

Cited by 2 publications

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Deep learning for automatic prediction of early activation of treatment naïve non-exudative MNVs in AMD

Crincoli,

Catania,

Sacconi

et al. 2024

Retina

View full text Add to dashboard Cite

Background: Around 30% of non-exudative macular neovascularizations(NE-MNVs) exudate within 2 years from diagnosis in patients with age-related macular degeneration(AMD).The aim of the study is to develop a deep learning classifier based on optical coherence tomography(OCT) and OCT angiography(OCTA) to identify NE-MNVs at risk of exudation. Methods: AMD patients showing OCTA and fluorescein angiography (FA) documented NE-MNV with a 2-years minimum imaging follow-up were retrospectively selected. Patients showing OCT B-scan-documented MNV exudation within the first 2 years formed the EX-GROUP while the others formed QU-GROUP.ResNet-101, Inception-ResNet-v2 and DenseNet-201 were independently trained on OCTA and OCT B-scan images. Combinations of the 6 models were evaluated with major and soft voting techniques. Results: Eighty-nine (89) eyes of 89 patients with a follow-up of 5.7 ± 1.5 years were recruited(35 EX GROUP and 54 QU GROUP). Inception-ResNet-v2 was the best performing among the 3 single convolutional neural networks(CNNs).The major voting model resulting from the association of the 3 different CNNs resulted in improvement of performance both for OCTA and OCT B-scan (both significantly higher than human graders’ performance). Soft voting model resulting from the combination of OCTA and OCT B-scan based major voting models showed a testing accuracy of 94.4%. Peripheral arcades and large vessels on OCTA enface imaging were more prevalent in QU GROUP. Conclusions: Artificial intelligence shows high performances in identifications of NE-MNVs at risk for exudation within the first 2 years of follow up, allowing better customization of follow up timing and avoiding treatment delay. Better results are obtained with the combination of OCTA and OCT B-scan image analysis.

show abstract

Deep learning for automatic prediction of early activation of treatment naïve non-exudative MNVs in AMD

Crincoli,

Catania,

Sacconi

et al. 2024

Retina

View full text Add to dashboard Cite

show abstract

Microvascular Changes in Treatment-Naïve Nonexudative Macular Neovascularization Complicated by Exudation

Crincoli,

Catania,

Labbate

et al. 2024

Retina

View full text Add to dashboard Cite

Purpose: To assess differences in choriocapillaris(CC) and macular neovascularization(MNV) optical coherence tomography angiography(OCTA)quantitative parameters between long-term persistently non-exudative MNVs(NE-MNVs)and long-term activated NE-MNVs in age-related macular degeneration(AMD) Materials and Methods: AMD patients with treatment-naïve NE-MNVs with >2 years follow-up and no evidence of exudation within the first 6-months from diagnosis were retrospectively recruited.Two groups were considered according to the occurrence(EX-GROUP) or not(NE-GROUP) of exudation within the first 2 years of follow-up.Segmentation of the MNV and of the perilesional CC were obtained from enface OCTA acquisitions at diagnosis and 6-months follow-up.OCT B-scan images of the MNV were also collected.Fractal ratio was defined as the ratio between MNV fractal dimension(FrD) and CC-FrD. Results: Fifty(50) eyes were included(20 EX-GROUP and 30 NE-GROUP).EX-GROUP showed higher flow deficit density(FDD) and flow deficit number(FDN) at 6-months follow-up.It also showed higher MNV FrD,lower CC-FrD and higher Fractal Ratio at 6-months follow-up.Fractal ratio significantly increased at 6-months acquisitions in EX-GROUP,showing an Area under the ROC curves(AUROC)of 0.887(CI 0.869-0.922). Conclusions: Fractal ratio at 6-months can predict exudation risk of MNV within 2 years from diagnosis.This suggests increased structural complexity of the NE-MNV accompanied by progressive capillary rarefaction of the perilesional CC as a key driving factor for development of exudation in NE-MNV.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Exudative Progression of Treatment-Naïve Nonexudative Macular Neovascularization in Age-Related Macular Degeneration: A Systematic Review With Meta-Analyses

Cited by 2 publications

References 36 publications

Deep learning for automatic prediction of early activation of treatment naïve non-exudative MNVs in AMD

Deep learning for automatic prediction of early activation of treatment naïve non-exudative MNVs in AMD

Microvascular Changes in Treatment-Naïve Nonexudative Macular Neovascularization Complicated by Exudation

Contact Info

Product

Resources

About