ArticleData in brief2020
Data on fundus images for vessels segmentation, detection of hypertensive retinopathy, diabetic retinopathy and papilledema.
Article in Data in brief, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Retinal manifestations in patients with SARS-CoV-2 infection and pathogenetic implications: a systematic review.International ophthalmology · 2022Pooled it
- A dataset of color fundus images for the detection and classification of eye diseases.Data in brief · 2024Article
- Multimodal dataset using OCTA and fundus images for the study of diabetic retinopathy.Data in brief · 2024Article
- Automatic Diagnosis of Glaucoma from Retinal Images Using Deep Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- The RETA Benchmark for Retinal Vascular Tree Analysis.Scientific data · 2022Article
- A new handheld fundus camera combined with visual artificial intelligence facilitates diabetic retinopathy screening.International journal of ophthalmology · 2022Article
- A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis.Frontiers in cardiovascular medicine · 2022Article
- Dataset from fundus images for the study of diabetic retinopathy.Data in brief · 2021Article
- Results from the SERPICO-19 study - the role of retinal evaluation and in vivo vascular assessment in COVID-19.EClinicalMedicine · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a dataset that contains 100 high quality fundus images which are acquired from Armed Forces Institute of Ophthalmology (AFIO), Rawalpindi Pakistan. The dataset has been marked by four expert ophthalmologists to aid clinicians and researchers in screening hypertensive retinopathy, diabetic retinopathy and papilledema cases. Moreover, it contains highly detailed annotations for retinal blood vascular patterns, arteries and veins to calculate arteriovenous ratio (AVR), optic nerve head (ONH) region and other retinal anomalies such as hard exudates and cotton wool spots etc. The dataset is extremely useful for the researchers who are working in the ophthalmic image analysis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.