Spatial Correspondence Between Intraretinal Fluid, Subretinal Fluid, and Pigment Epithelial Detachment in Neovascular Age-Related Macular Degeneration

Klimscha, Sophie; Waldstein, Sebastian M.; Schlegl, Thomas; Bogunović, Hrvoje; Sadeghipour, Amir; Philip, Ana-Maria; Podkowinski, Dominika; Pablik, Eleonore; Zhang, Li; Abràmoff, Michael D.; Sonka, Milan; Gerendas, Bianca S.; Schmidt‐Erfurth, Ursula

doi:10.1167/iovs.16-20201

Cited by 31 publications

(22 citation statements)

References 21 publications

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“…Because IRF and SRF appear spatially separated, with IRF seen foveally and SRF existing largely beyond the central 1 mm, the absolute amount of SRF is systematically underestimated. 36 The large volume of baseline SRF noted by quantitative AI was a surprise.…”

Section: Discussionmentioning

confidence: 99%

Application of Automated Quantification of Fluid Volumes to Anti–VEGF Therapy of Neovascular Age-Related Macular Degeneration

et al. 2020

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Purpose: Antievascular endothelial growth factor (VEGF) treatment of neovascular age-related macular degeneration (AMD) is a highly effective advance in the retinal armentarium. OCT offering 3-dimensional imaging of the retina is widely used to guide treatment. Although poor outcomes reported from clinical practice are multifactorial, availability of reliable, reproducible, and quantitative evaluation tools to accurately measure the fluid response, that is, a "VEGF meter," may be a better means of monitoring and treating than the current purely qualitative evaluation used in clinical practice.Design: Post hoc analysis of a phase III, randomized, multicenter study.Participants: Study eyes of 1095 treatment-naive subjects receiving pro re nata (PRN) or monthly ranibizumab therapy according to protocol-specified criteria in the HARBOR study.Methods: A deep learning method for localization and quantification of fluid in all retinal compartments was applied for automated segmentation of fluid with every voxel classified by a convolutional neural network (CNN). Three-dimensional volumes (nanoliters) for intraretinal fluid (IRF), subretinal fluid (SRF), and pigment epithelial detachment (PED) were determined in 24 362 volume scans obtained from 1095 patients treated over 24 months in a phase III clinical trial with randomization to 2 drug dosages (0.5 mg and 2.0 mg ranibizumab) and 2 regimens (monthly and PRN). A multivariable mixed-effects regression model was used to test for differences in fluid between the arms and for fluid/function correlation.Main Outcome Measures: Fluid volume in nanoliters, structure-function as Pearson's correlation coefficient, and as a coefficient of determination (R 2 ).Results: Fluid volumes were quantified in all visits of all patients. Automated segmentation demonstrated characteristic response patterns for each fluid compartment individually: Intraretinal fluid showed the greatest and most rapid resolution, followed by SRF and PED the least. The loading dose treatment achieved resolution of all fluid types close to the lowest levels attainable. Dosage and regimen parameters correlated directly with resulting fluid volumes. Fluid/function correlation showed a volume-dependent negative impact of IRF on vision and weak positive prognostic effect of SRF.Conclusions: Automated quantification of the fluid response may improve therapeutic management of neovascular AMD, avoid discrepancies between clinicians/investigators, and establish structure/function correlations.

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Section: Discussionmentioning

confidence: 99%

Application of Automated Quantification of Fluid Volumes to Anti–VEGF Therapy of Neovascular Age-Related Macular Degeneration

et al. 2020

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“…Similarly, thickness assessments have typically used bulk measurements per macula, single measurements at the foveal center, or areas or volumes measured within different diameter circles centered on the fovea [18][19][20][21] . Some studies have evaluated co-localized fluid and PED featured in nAMD 22 . In this study, we report the first analysis of OCT features by precise macular location and extent relative to components identified on CP/FA.…”

Section: Discussionmentioning

confidence: 99%

Distribution of OCT Features within Areas of Macular Atrophy or Scar after 2 Years of Anti-VEGF Treatment for Neovascular AMD in CATT

Toth

Tai

Pistilli

et al. 2019

Ophthalmology Retina

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Purpose: Macular atrophy and scar increase in prevalence during treatment for neovascular agerelated macular degeneration (AMD) and are associated with poor visual acuity. We sought to identify the distribution of spectral domain optical coherence tomography (SDOCT)-determined features and subretinal lesion thicknesses at sites of macular scar or atrophy after two years of treatment in the Comparison of AMD Treatments Trials (CATT).

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“…AI has been applied to various adult ophthalmic diseases, including diabetic retinopathy [1,[74][75][76][77], AMD [78][79][80][81][82][83], sight-threatening retinal disease [2,[84][85][86][87][88][89], glaucoma [90][91][92], intraocular lens calculation [93], and keratoconus [94]. It has also been used for robotassisted repair of epiretinal membranes [95], retinal vessel segmentation [96][97][98][99], and systemic disease prediction from fundus images [100].…”

Section: Non-pediatric Applicationsmentioning

confidence: 99%

Artificial intelligence for pediatric ophthalmology

Reid

Eaton

2019

Current Opinion in Ophthalmology

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Purpose of reviewDespite the impressive results of recent artificial intelligence (AI) applications to general ophthalmology, comparatively less progress has been made toward solving problems in pediatric ophthalmology using similar techniques. This article discusses the unique needs of pediatric ophthalmology patients and how AI techniques can address these challenges, surveys recent applications of AI to pediatric ophthalmology, and discusses future directions in the field. Recent findingsThe most significant advances involve the automated detection of retinopathy of prematurity (ROP), yielding results that rival experts. Machine learning (ML) has also been successfully applied to the classification of pediatric cataracts, prediction of post-operative complications following cataract surgery, detection of strabismus and refractive error, prediction of future high myopia, and diagnosis of reading disability via eye tracking. In addition, ML techniques have been used for the study of visual development, vessel segmentation in pediatric fundus images, and ophthalmic image synthesis. SummaryAI applications could significantly benefit clinical care for pediatric ophthalmology patients by optimizing disease detection and grading, broadening access to care, furthering scientific discovery, and improving clinical efficiency. These methods need to match or surpass physician performance in clinical trials before deployment with patients. Due to widespread use of closed-access data sets and software implementations, it is difficult to directly compare the performance of these approaches, and reproducibility is poor. Open-access data sets and software implementations could alleviate these issues, and encourage further AI applications to pediatric ophthalmology.

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Spatial Correspondence Between Intraretinal Fluid, Subretinal Fluid, and Pigment Epithelial Detachment in Neovascular Age-Related Macular Degeneration

Cited by 31 publications

References 21 publications

Application of Automated Quantification of Fluid Volumes to Anti–VEGF Therapy of Neovascular Age-Related Macular Degeneration

Application of Automated Quantification of Fluid Volumes to Anti–VEGF Therapy of Neovascular Age-Related Macular Degeneration

Distribution of OCT Features within Areas of Macular Atrophy or Scar after 2 Years of Anti-VEGF Treatment for Neovascular AMD in CATT

Artificial intelligence for pediatric ophthalmology

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