Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-14T12:49:36.691457Z
Paper Citation Record · LEDGER
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study
As of 16 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 0 inbound Pith citation observations for arXiv:1908.06399.
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
14 of 14 outbound references displayed
- verified exact0
- verified fuzzy12
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 7e242978-8f73-4c79-ba24-117c3abea554 · outbound
This paper cites Diabetes prevention: global health policy and perspectives from the ground.
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study Diabetes prevention: global health policy and perspectives from the ground
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:49:36.859656Z
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 df7c2ba7-1a7c-4df3-8219-21e714c24238 · outbound
This paper cites Workforce Census, November 2016 {Available at https://www.rcophth.ac.uk/wp-content/uploads/2017/03/RCOphth-Workforce-Census -2016.pdf , accessed 17th October 2018}.
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study Workforce Census, November 2016 {Available at https://www.rcophth.ac.uk/wp-content/uploads/2017/03/RCOphth-Workforce-Census -2016.pdf , accessed 17th October 2018}
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:49:36.850314Z
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 b1db2614-127b-4f07-90e5-445b0cce22c7 · outbound
This paper cites The number of ophthalmologists in practice and training worldwide: a growing gap despite more than 200 000 practitioners.
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study The number of ophthalmologists in practice and training worldwide: a growing gap despite more than 200 000 practitioners
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:49:36.840713Z
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 09a314ce-1f25-481d-8285-4760530bbeaf · outbound
This paper cites Dermatologist-level classification of skin cancer with deep neural networks.
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study Dermatologist-level classification of skin cancer with deep neural networks
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:49:36.830455Z
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 bf6bdb4a-6ffa-48a7-a2bf-0b6ac86fe247 · outbound
This paper cites Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer.
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:49:36.820036Z
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 93563a58-5f20-40da-9080-b3be6a90ae7a · outbound
This paper cites Retinal imaging and image analysis.
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study Retinal imaging and image analysis
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:49:36.809851Z
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 06d40b00-74af-431c-af55-54bcddd4d2b8 · outbound
This paper cites Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research.
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study Indian diabetic retinopathy image dataset (IDRiD): a database for diabetic retinopathy screening research
Reference 7
- Resolution
- unresolved
- no resolver link, observed 2026-08-14T12:49:36.665076Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 383e0767-09c4-4eb1-b6e5-d272f8eba3f9 · outbound
This paper cites Quality and learning curve of handheld versus stand-alone non-mydriatic cameras.
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study Quality and learning curve of handheld versus stand-alone non-mydriatic cameras
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:49:36.799598Z
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 3a594578-1115-47c0-a8e1-5f25dd0ba9d6 · outbound
This paper cites Grading diabetic retinopathy (DR) using the Scottish grading protocol.
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study Grading diabetic retinopathy (DR) using the Scottish grading protocol
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:49:36.788126Z
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 27d93c73-1fc4-4fbe-8186-45934b4b53be · outbound
This paper cites Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence.
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study Automated diabetic retinopathy detection in smartphone-based fundus photography using artificial intelligence
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:49:36.765178Z
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 96117079-1f8a-4813-9ecc-e2f29b6fff7d · outbound
This paper cites Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders.
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:49:36.752524Z
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 6f8d4af0-a3e3-4af3-aa4a-850a973fcd15 · outbound
This paper cites Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes.
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:49:36.740539Z
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 83a41298-cf48-419c-83d6-1e3e531c236b · outbound
This paper cites Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-14T12:49:36.729319Z
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 bd1b766c-1a8f-4b01-ba6f-ad7e427c822d · outbound
This paper cites an unresolved cited work.
Evaluation of an AI System for the Detection of Diabetic Retinopathy from Images Captured with a Handheld Portable Fundus Camera: the MAILOR AI study Unresolved cited work
Reference 2016
- Resolution
- unresolved
- raw_fallback, observed 2026-08-14T12:49:36.776987Z
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.