Deep Learning Applied to Automated Segmentation of Geographic Atrophy in Fundus Autofluorescence Images

Arslan, Janan; Samarasinghe, Gihan; Sowmya, Arcot; Benke, Kurt K.; Hodgson, Lauren; Guymer, Robyn H.; Baird, Paul N.

doi:10.1167/tvst.10.8.2

Cited by 12 publications

(15 citation statements)

References 51 publications

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“…Different approaches for automated GA segmentation on various imaging modalities have been proposed previously 27 . Most studies predominantly focused on GA segmentation on 2D FAF with a DSC ranging from 0.83 to 0.89 28 , 29 . As SD-OCT has become the standard imaging method in AMD in the clinical setting 7 , more studies have now focused on GA segmentation on OCT 30 – 32 with a DSC range of 0.81–0.87 27 .…”

Section: Discussionmentioning

confidence: 99%

Clinical validation for automated geographic atrophy monitoring on OCT under complement inhibitory treatment

Mai

Lachinov

Riedl

et al. 2023

Sci Rep

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Geographic atrophy (GA) represents a late stage of age-related macular degeneration, which leads to irreversible vision loss. With the first successful therapeutic approach, namely complement inhibition, huge numbers of patients will have to be monitored regularly. Given these perspectives, a strong need for automated GA segmentation has evolved. The main purpose of this study was the clinical validation of an artificial intelligence (AI)-based algorithm to segment a topographic 2D GA area on a 3D optical coherence tomography (OCT) volume, and to evaluate its potential for AI-based monitoring of GA progression under complement-targeted treatment. 100 GA patients from routine clinical care at the Medical University of Vienna for internal validation and 113 patients from the FILLY phase 2 clinical trial for external validation were included. Mean Dice Similarity Coefficient (DSC) was 0.86 ± 0.12 and 0.91 ± 0.05 for total GA area on the internal and external validation, respectively. Mean DSC for the GA growth area at month 12 on the external test set was 0.46 ± 0.16. Importantly, the automated segmentation by the algorithm corresponded to the outcome of the original FILLY trial measured manually on fundus autofluorescence. The proposed AI approach can reliably segment GA area on OCT with high accuracy. The availability of such tools represents an important step towards AI-based monitoring of GA progression under treatment on OCT for clinical management as well as regulatory trials.

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Section: Discussionmentioning

confidence: 99%

Clinical validation for automated geographic atrophy monitoring on OCT under complement inhibitory treatment

Mai

Lachinov

Riedl

et al. 2023

Sci Rep

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“…Usually, blue-light FAF (488 nm excitation wavelength) is most commonly used for imaging AMD, however longer wavelengths, such as green or near infrared, have shown advantages in detecting subtle changes. Recently, AI-based algorithms applied on FAF imaging were developed with the potential of automated segment macular lesions in GA [ 28 , 29 ]. However, FAF devices are not readily available in clinical practices.…”

Section: Ai In Retinal Imagingmentioning

confidence: 99%

“…Therefore, FDA and European Medicines Agencies accepted FAF-based measurements of changes in GA area as anatomical endpoint in the early clinical trials [ 84 , 85 ]. Availability of high-resolution three-dimensional OCT imaging together with AI tools detecting the pathognomonic neurosensory, yet subclinical features not accessible to human specialists by retinal images alone, but accurately visualized on OCT-based AI analysis represent the novel horizon of precision medicine [ 28 , 29 , 86 ].…”

Section: Ai Techniques In Oct Analysismentioning

confidence: 99%

AI-based support for optical coherence tomography in age-related macular degeneration

Mares,

Nehemy,

Bogunovic

et al. 2024

Int J Retin Vitr

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Artificial intelligence (AI) has emerged as a transformative technology across various fields, and its applications in the medical domain, particularly in ophthalmology, has gained significant attention. The vast amount of high-resolution image data, such as optical coherence tomography (OCT) images, has been a driving force behind AI growth in this field. Age-related macular degeneration (AMD) is one of the leading causes for blindness in the world, affecting approximately 196 million people worldwide in 2020. Multimodal imaging has been for a long time the gold standard for diagnosing patients with AMD, however, currently treatment and follow-up in routine disease management are mainly driven by OCT imaging. AI-based algorithms have by their precision, reproducibility and speed, the potential to reliably quantify biomarkers, predict disease progression and assist treatment decisions in clinical routine as well as academic studies. This review paper aims to provide a summary of the current state of AI in AMD, focusing on its applications, challenges, and prospects.

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“…Artificial intelligence (AI)–based approaches to detect and quantify GA lesions could address a significant unmet need to precisely determine GA lesion size, enable efficient monitoring of GA progression over time, and may facilitate AI-based predictions of future GA lesion growth rates. 7 – 9 Automated GA segmentation algorithms that use retinal images obtained using different modalities, including FAF, 5 , 10 , 11 OCT, 12 – 14 and NIR, 15 as well as multimodal approaches using combinations of different imaging techniques, 16 have been described previously. 17 Algorithms using k -nearest-neighbor pixel classifiers, 5 Fuzzy-c-means (a clustering algorithm), 7 , 17 or deep convolutional neural networks (CNNs) 11 , 18 led to good agreement with manual segmentation performed by trained graders.…”

Section: Introductionmentioning

confidence: 99%

Geographic Atrophy Segmentation Using Multimodal Deep Learning

Spaide¹,

Jiang

Patil³

et al. 2023

Trans. Vis. Sci. Tech.

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Purpose To examine deep learning (DL)–based methods for accurate segmentation of geographic atrophy (GA) lesions using fundus autofluorescence (FAF) and near-infrared (NIR) images. Methods This retrospective analysis utilized imaging data from study eyes of patients enrolled in Proxima A and B (NCT02479386; NCT02399072) natural history studies of GA. Two multimodal DL networks (UNet and YNet) were used to automatically segment GA lesions on FAF; segmentation accuracy was compared with annotations by experienced graders. The training data set comprised 940 image pairs (FAF and NIR) from 183 patients in Proxima B; the test data set comprised 497 image pairs from 154 patients in Proxima A. Dice coefficient scores, Bland–Altman plots, and Pearson correlation coefficient ( r ) were used to assess performance. Results On the test set, Dice scores for the DL network to grader comparison ranged from 0.89 to 0.92 for screening visit; Dice score between graders was 0.94. GA lesion area correlations ( r ) for YNet versus grader, UNet versus grader, and between graders were 0.981, 0.959, and 0.995, respectively. Longitudinal GA lesion area enlargement correlations ( r ) for screening to 12 months ( n = 53) were lower (0.741, 0.622, and 0.890, respectively) compared with the cross-sectional results at screening. Longitudinal correlations ( r ) from screening to 6 months ( n = 77) were even lower (0.294, 0.248, and 0.686, respectively). Conclusions Multimodal DL networks to segment GA lesions can produce accurate results comparable with expert graders. Translational Relevance DL-based tools may support efficient and individualized assessment of patients with GA in clinical research and practice.

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Deep Learning Applied to Automated Segmentation of Geographic Atrophy in Fundus Autofluorescence Images

Cited by 12 publications

References 51 publications

Clinical validation for automated geographic atrophy monitoring on OCT under complement inhibitory treatment

Clinical validation for automated geographic atrophy monitoring on OCT under complement inhibitory treatment

AI-based support for optical coherence tomography in age-related macular degeneration

Geographic Atrophy Segmentation Using Multimodal Deep Learning

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