A column-based deep learning method for the detection and quantification of atrophy associated with AMD in OCT scans

Purpose The purpose of this study was to evaluate the long-term rate of progression and baseline predictors of geographic atrophy (GA) using complete retinal pigment epithelium and outer retinal atrophy (cRORA) annotation criteria. Methods This is a retrospective study. Columns of GA were manually annotated by two graders using a self-developed software on optical coherence tomography (OCT) B-scans and projected onto the infrared images. The primary outcomes were: (1) rate of area progression, (2) rate of square root area progression, and (3) rate of radial progression towards the fovea. The effects of 11 additional baseline predictors on the primary outcomes were analyzed: total area, focality (defined as the number of lesions whose area is >0.05 mm 2 ), circularity, total lesion perimeter, minimum diameter, maximum diameter, minimum distance from the center, sex, age, presence/absence of hypertension, and lens status. Results GA was annotated in 33 pairs of baseline and follow-up OCT scans from 33 eyes of 18 patients with dry age-related macular degeneration (AMD) followed for at least 6 months. The mean rate of area progression was 1.49 ± 0.86 mm 2 /year ( P < 0.0001 vs. baseline), and the mean rate of square root area progression was 0.33 ± 0.15 mm/year ( P < 0.0001 vs. baseline). The mean rate of radial progression toward the fovea was 0.07 ± 0.11 mm/year. A multiple variable linear regression model (adjusted r 2 = 0.522) revealed that baseline focality and female sex were significantly correlated with the rate of GA area progression. Conclusions GA area progression was quantified using OCT as an alternative to conventional measurements performed on fundus autofluorescence images. Baseline focality correlated with GA area progression rate and lesion's minimal distance from the center correlated with GA radial progression rate toward the center. These may be important markers for the assessment of GA activity. Translational Relevance Advanced method linking specific retinal micro-anatomy to GA area progression analysis.

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“…Our custom software program was used to manually annotate GA in the baseline and follow-up OCT B-scans. 5 …”

Section: Methodsmentioning

confidence: 99%

“…In previous work, we developed a custom image analysis software platform for measuring GA progression in OCT scans using cRORA criteria for GA annotation. 5 Here, we used this system to annotate and quantify the rate of GA progression in patients with GA secondary to AMD and to identify potential baseline predictors of GA progression.…”

Section: Introductionmentioning

confidence: 99%

Progression of cRORA (Complete RPE and Outer Retinal Atrophy) in Dry Age-Related Macular Degeneration Measured Using SD-OCT

Shmueli

Yehuda

Szeskin

et al. 2022

Trans. Vis. Sci. Tech.

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“…From Figure 3 b, the gaps in the IR image between the labeled B-scans become apparent. Szeskin et al [ 16 ] used morphological dilation and closing operations with experimentally determined parameters for closing the gaps. We found that this approach does not work well in our case, where OCT slices only number 25 of them compared with the 40–80 in [ 16 ] (compare Figure 3 d).…”

Section: Materials and Methodsmentioning

confidence: 99%

“…Previous works have proposed several methods for the registration of FAF and other en face imaging modalities [ 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Similarly, efforts have been made to visualize OCT labels, e.g., by projecting them onto the accompanying en face infrared (IR) image, where B-scan positions are marked by the OCT device [ 16 , 17 ] or a joint reference space [ 18 ]. In [ 19 ] an en face image is created by reducing the OCT volume.…”

Section: Introductionmentioning

confidence: 99%

Chronological Registration of OCT and Autofluorescence Findings in CSCR: Two Distinct Patterns in Disease Course

et al. 2022

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Optical coherence tomography (OCT) and fundus autofluorescence (FAF) are important imaging modalities for the assessment and prognosis of central serous chorioretinopathy (CSCR). However, setting the findings from both into spatial and temporal contexts as desirable for disease analysis remains a challenge due to both modalities being captured in different perspectives: sparse three-dimensional (3D) cross sections for OCT and two-dimensional (2D) en face images for FAF. To bridge this gap, we propose a visualisation pipeline capable of projecting OCT labels to en face image modalities such as FAF. By mapping OCT B-scans onto the accompanying en face infrared (IR) image and then registering the IR image onto the FAF image by a neural network, we can directly compare OCT labels to other labels in the en face plane. We also present a U-Net inspired segmentation model to predict segmentations in unlabeled OCTs. Evaluations show that both our networks achieve high precision (0.853 Dice score and 0.913 Area under Curve). Furthermore, medical analysis performed on exemplary, chronologically arranged CSCR progressions of 12 patients visualized with our pipeline indicates that, on CSCR, two patterns emerge: subretinal fluid (SRF) in OCT preceding hyperfluorescence (HF) in FAF and vice versa.

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“…As a possible solution for the screening of major ophthalmic diseases and telemedicine, AI has been applied to the study of ophthalmic disease diagnosis. For example, diabetic retinopathy, glaucoma, hypertensive retinopathy, high myopia, age-related macular degeneration, familial amyloidosis, cataract, and other related conditions have been investigated using this technology ( Fang et al, 2017 ; Asaoka et al, 2019 ; Araujo et al, 2020 ; Juneja et al, 2020 ; Kessel et al, 2020 ; Zhou et al, 2020 ; Wan et al, 2021a ; Grzybowski and Brona, 2021 ; Szeskin et al, 2021 ; Xu et al, 2022 ). AI may have an impact on medical and ophthalmic practice in the coming decades, based on the results of several published reports ( Ting et al, 2019 ; Dai et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis

Zhao

Zhu

et al. 2022

Front. Pharmacol.

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Background: Artificial intelligence (AI) has been used in the research of ophthalmic disease diagnosis, and it may have an impact on medical and ophthalmic practice in the future. This study explores the general application and research frontier of artificial intelligence in ophthalmic disease detection.Methods: Citation data were downloaded from the Web of Science Core Collection database to evaluate the extent of the application of Artificial intelligence in ophthalmic disease diagnosis in publications from 1 January 2012, to 31 December 2021. This information was analyzed using CiteSpace.5.8. R3 and Vosviewer.Results: A total of 1,498 publications from 95 areas were examined, of which the United States was determined to be the most influential country in this research field. The largest cluster labeled “Brownian motion” was used prior to the application of AI for ophthalmic diagnosis from 2007 to 2017, and was an active topic during this period. The burst keywords in the period from 2020 to 2021 were system, disease, and model.Conclusion: The focus of artificial intelligence research in ophthalmic disease diagnosis has transitioned from the development of AI algorithms and the analysis of abnormal eye physiological structure to the investigation of more mature ophthalmic disease diagnosis systems. However, there is a need for further studies in ophthalmology and computer engineering.

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A column-based deep learning method for the detection and quantification of atrophy associated with AMD in OCT scans

Cited by 14 publications

References 21 publications

Progression of cRORA (Complete RPE and Outer Retinal Atrophy) in Dry Age-Related Macular Degeneration Measured Using SD-OCT

Progression of cRORA (Complete RPE and Outer Retinal Atrophy) in Dry Age-Related Macular Degeneration Measured Using SD-OCT

Chronological Registration of OCT and Autofluorescence Findings in CSCR: Two Distinct Patterns in Disease Course

Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis

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