Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T13:11:33.282706Z
Paper Citation Record · LEDGER
A deep learning model for segmentation of geographic atrophy to study its long-term natural history
As of 16 August 2026, this Paper Citation Record lists 45 of 45 outbound references and 0 inbound Pith citation observations for arXiv:1908.05621.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-16T06:30:59.297886+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
45 of 45 outbound references displayed
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External citation measurements
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Outbound references
Observation 4fd13a22-c8a7-4c66-9760-b0f0e829a7b3 · outbound
This paper cites Age-related macular degeneration.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Age-related macular degeneration
Reference 1
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Observation 07be96ff-b889-4740-984a-6218b70b63a8 · outbound
This paper cites Visual function abnormalities and prognosis in eyes with age -related geographic atrophy of the macula and good visual acuity.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Visual function abnormalities and prognosis in eyes with age -related geographic atrophy of the macula and good visual acuity
Reference 2
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Observation 3b9cfee6-549a-471f-94c9-6a8fdab150b5 · outbound
This paper cites The estimated prevalence and incidence of late stage age related macular degeneration in the UK.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history The estimated prevalence and incidence of late stage age related macular degeneration in the UK
Reference 3
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Observation 9408ade8-5eb0-468d-ab5a-85e2bcf4f8e5 · outbound
This paper cites Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta- analysis.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and meta- analysis
Reference 4
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Observation 8144e46b-5781-40d7-abf6-e56bed9b6f5f · outbound
This paper cites Prevalence of age -related macular degeneration in Europe: the past and the fut ure.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Prevalence of age -related macular degeneration in Europe: the past and the fut ure
Reference 5
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Observation 6968c030-be02-47b6-954b-a3ac3fcd8289 · outbound
This paper cites Age-related macular degeneration— emerging pathogenetic and therapeutic concepts.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Age-related macular degeneration— emerging pathogenetic and therapeutic concepts
Reference 6
- Resolution
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Observation c4713de9-bb7e-4141-86c9-f9a62acc28fc · outbound
This paper cites The pathophysiology of geographic atrophy secondary to age -related macular degeneration and the complement pathway as a therapeutic target.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history The pathophysiology of geographic atrophy secondary to age -related macular degeneration and the complement pathway as a therapeutic target
Reference 7
- Resolution
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Observation b2156a3a-da29-4b58-ad3d-eeb4135bece7 · outbound
This paper cites Current therapeutic developments in atrophic age - related macular degeneration.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Current therapeutic developments in atrophic age - related macular degeneration
Reference 8
- Resolution
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Observation d7bab9da-75c3-4f60-b386-778fce98fbf6 · outbound
This paper cites Geographic atrophy: clinical features and potential therapeutic approaches.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Geographic atrophy: clinical features and potential therapeutic approaches
Reference 9
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Source-reported events for the cited work
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Observation 2db33e72-d5f7-497a-912e-74d9ddb26cc3 · outbound
This paper cites Designin g clinical trials for age - related geographic atrophy of the macula: enrollment data from the geographic atrophy natural history study.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Designin g clinical trials for age - related geographic atrophy of the macula: enrollment data from the geographic atrophy natural history study
Reference 10
- Resolution
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 7c6e9ffd-639b-4882-ba47-a30e6bd60758 · outbound
This paper cites Change in area of geographic atrophy in the Age -Related Eye Disease Study: AREDS report number 26.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Change in area of geographic atrophy in the Age -Related Eye Disease Study: AREDS report number 26
Reference 11
- Resolution
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Source-reported events for the cited work
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Observation 8d47a3c7-a676-43f4-8e82-27a7bddffb91 · outbound
This paper cites The progression of geographic atrophy secondary to age -related macular degeneration.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history The progression of geographic atrophy secondary to age -related macular degeneration
Reference 12
- Resolution
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Observation c404da52-5552-42bd-a297-a436987b662d · outbound
This paper cites Natural history of geographic atrophy progression secondary to age-related macular degeneration (Geographic Atrophy Progression Study).
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Natural history of geographic atrophy progression secondary to age-related macular degeneration (Geographic Atrophy Progression Study)
Reference 13
- Resolution
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Observation 6c170392-c532-461a-b28a-5e9b213eef07 · outbound
This paper cites Geographic atrophy in patients with advanced dry age -related macular degeneration: current challenges and future prospects.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Geographic atrophy in patients with advanced dry age -related macular degeneration: current challenges and future prospects
Reference 14
- Resolution
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Source-reported events for the cited work
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Observation 5757aabf-95b0-42b1-8875-c0c825b1616e · outbound
This paper cites Measuring geographic atrophy in advanced age -related macular degen eration.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Measuring geographic atrophy in advanced age -related macular degen eration
Reference 15
- Resolution
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Observation ab83cccf-76ed-45b9-956f-db5d48ae230e · outbound
This paper cites Progression of geographic atrophy in age-related macular degeneration imaged with spectral domain optical coherence tomography.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Progression of geographic atrophy in age-related macular degeneration imaged with spectral domain optical coherence tomography
Reference 16
- Resolution
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Observation 7f40ca48-1897-4c66-bb1c-44552444a6ac · outbound
This paper cites A survey on deep learning in medical image analysis.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history A survey on deep learning in medical image analysis
Reference 17
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Source-reported events for the cited work
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Observation e583f404-3b83-4fb1-9a2a-794cb6613895 · outbound
This paper cites Automated grading of age -related macular degeneration from color fundus images using deep c onvolutional neural networks.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Automated grading of age -related macular degeneration from color fundus images using deep c onvolutional neural networks
Reference 18
- Resolution
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Source-reported events for the cited work
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Observation 5fbb37e9-5cad-4d6a-b9a2-4207ca6a48e8 · outbound
This paper cites DeepSeeNet: a deep learning model for automated classification of patient -based age -related macular degeneration severity from color fundus photographs.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history DeepSeeNet: a deep learning model for automated classification of patient -based age -related macular degeneration severity from color fundus photographs
Reference 19
- Resolution
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Observation 74209bc5-1fac-45bb-8fff-de082e268fe5 · outbound
This paper cites Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Reference 20
- Resolution
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Observation 0da0f9ce-d95e-4042-88f9-ae0e3538599a · outbound
This paper cites A d eep learning approach for automated detection of geographic atrophy from color fundus photographs.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history A d eep learning approach for automated detection of geographic atrophy from color fundus photographs
Reference 21
- Resolution
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Observation 640b973a-a261-487b-afd4-b506565f0b8b · outbound
This paper cites The epidemiology of progression of pure geographic atrophy: the Beaver Dam Eye Study.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history The epidemiology of progression of pure geographic atrophy: the Beaver Dam Eye Study
Reference 22
- Resolution
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Observation 4730ab5c-fbd1-47df-817f-2b5c7c6cd926 · outbound
This paper cites Circularity index as a risk factor for progression of geographic atrophy.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Circularity index as a risk factor for progression of geographic atrophy
Reference 23
- Resolution
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Observation 4a52f4e7-3350-4ecd-ad8e-2271d1e0dff8 · outbound
This paper cites The long -term natural history of geographic atrophy from age -related macular degeneration: enlargement of atrophy and implications for interventional clinical trials.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history The long -term natural history of geographic atrophy from age -related macular degeneration: enlargement of atrophy and implications for interventional clinical trials
Reference 24
- Resolution
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Observation f404c74f-39a3-487a-80e5-970353f5a99f · outbound
This paper cites Progression of geographic atrophy and impact of fundus autofluorescence patterns in age -related macular degeneration.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Progression of geographic atrophy and impact of fundus autofluorescence patterns in age -related macular degeneration
Reference 25
- Resolution
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Observation 4bf3a832-3b7e-44c2-8025-175e1d984d7f · outbound
This paper cites an unresolved cited work.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Unresolved cited work
Reference 26
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Observation 9fb9c0ce-4dbd-4293-92c6-e9cf3e918269 · outbound
This paper cites 2013;131:110-111.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history 2013;131:110-111
Reference 27
- Resolution
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Observation 1cb33167-429f-476a-a9b4-b3384d46bac5 · outbound
This paper cites Natural history of geographic atrophy in untreated eyes with nonexudative age -related macular degeneration: a systematic review and meta-analysis.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Natural history of geographic atrophy in untreated eyes with nonexudative age -related macular degeneration: a systematic review and meta-analysis
Reference 28
- Resolution
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Observation 80d0fc65-ae27-4ce9-bafc-62223fd151a0 · outbound
This paper cites Fundus autofluorescence and spectral -domain optical coherence tomography characteristics in a rapi dly progressing form of geographic atrophy.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Fundus autofluorescence and spectral -domain optical coherence tomography characteristics in a rapi dly progressing form of geographic atrophy
Reference 29
- Resolution
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Observation 9f7c2a8e-f942-41c8-8641-62e183798c1d · outbound
This paper cites Evaluation of geographic atrophy from color photographs and fundus autofluorescence images: Age -Related Eye Disease Study 2 Report Number 11.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Evaluation of geographic atrophy from color photographs and fundus autofluorescence images: Age -Related Eye Disease Study 2 Report Number 11
Reference 30
- Resolution
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Observation 0c572514-3a2f-43dd-86af-1b3e81e7e9c8 · outbound
This paper cites Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD -OCT images.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD -OCT images
Reference 31
- Resolution
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 79681967-8601-4717-a604-cfea83d8d671 · outbound
This paper cites Segmentation of the geographic atrophy in spectral -domain optical coherence tomography and fundus autofluorescence images.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Segmentation of the geographic atrophy in spectral -domain optical coherence tomography and fundus autofluorescence images
Reference 32
- Resolution
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 409e851d-c25a-4925-9687-aa630079dcf3 · outbound
This paper cites Automated geographic atrophy segmentation for SD -OCT images using region -based CV model via local similarity factor.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Automated geographic atrophy segmentation for SD -OCT images using region -based CV model via local similarity factor
Reference 33
- Resolution
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 8a272172-5934-4983-9f4c-390e0e03dbfb · outbound
This paper cites Automated segmentation of geographic atrophy in fundus autofluorescence images using supervised pixel classification.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Automated segmentation of geographic atrophy in fundus autofluorescence images using supervised pixel classification
Reference 34
- Resolution
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No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6179891d-d5af-4dcc-a058-1c71383f04fe · outbound
This paper cites Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Automated segmentation of geographic atrophy of the retinal epithelium via random forests in AREDS color fundus images
Reference 35
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3a3676ea-abc1-42c0-b11c-6d1ddd7d7e8a · outbound
This paper cites Prevalence of age -related maculopathy in Australia.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Prevalence of age -related maculopathy in Australia
Reference 36
- Resolution
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Source-reported events for the cited work
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Observation 9a7686b7-2860-47ee-ad0b-f18d2bbcba6f · outbound
This paper cites The Rotterdam Study: 2018 update on objectives, design and main results.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history The Rotterdam Study: 2018 update on objectives, design and main results
Reference 37
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation e0113db5-d082-4d2c-b5e6-61243df4be41 · outbound
This paper cites Growth of geographic atrophy in the comparison of age -related macular degeneration treatments trials.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Growth of geographic atrophy in the comparison of age -related macular degeneration treatments trials
Reference 38
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 984ea9f5-ccac-41dd-b085-5f6622f919b8 · outbound
This paper cites EyeNED workstation: development of a multi-modal vendor-independent application for annotation, spatial alignment and analysis of retinal images.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history EyeNED workstation: development of a multi-modal vendor-independent application for annotation, spatial alignment and analysis of retinal images
Reference 39
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 38c7bfd8-d897-48d1-a6c8-5d3327a81508 · outbound
This paper cites Clinical ly applicable deep learning for diagnosis and referral in retinal disease.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Clinical ly applicable deep learning for diagnosis and referral in retinal disease
Reference 40
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 3f6f6995-505e-4154-b067-fcea0856a9df · outbound
This paper cites Kaggle diabetic retinopathy detection competition report.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Kaggle diabetic retinopathy detection competition report
Reference 41
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation a78a0039-fd87-476d-aa2f-5bc4591c9cbe · outbound
This paper cites Progression of geogra phic atrophy in age-related macular degeneration: AREDS2 report number 16.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Progression of geogra phic atrophy in age-related macular degeneration: AREDS2 report number 16
Reference 42
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6188560a-cef8-45b7-9976-d8e621f962cc · outbound
This paper cites Circularity, solidity, axes of a best fit ellipse, aspect ratio, and roundness of the foramen ovale: a morphometric analysis with neurosurgical considerations.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Circularity, solidity, axes of a best fit ellipse, aspect ratio, and roundness of the foramen ovale: a morphometric analysis with neurosurgical considerations
Reference 43
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation cde41fdf-1900-41c4-9b73-0af814d1dc95 · outbound
This paper cites et al., Keras, https://keras.io, 2015.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history et al., Keras, https://keras.io, 2015
Reference 44
- Resolution
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Observation 6534de12-b395-41db-b04b-75862e1700a8 · outbound
This paper cites an unresolved cited work.
A deep learning model for segmentation of geographic atrophy to study its long-term natural history Unresolved cited work
Reference 45
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Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.
Pith citing papers
No inbound Pith citation observations are available.