ArticleJournal of diabetes research2015
Combined Methods for Diabetic Retinopathy Screening, Using Retina Photographs and Tear Fluid Proteomics Biomarkers.
Article in Journal of diabetes research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 43 citations in OpenAlex.
- Pooled it
- A panoramic perspective: application prospects and outlook of multimodal artificial intelligence in the management of diabetic retinopathy.Frontiers in public health · 2025Review
- New insights of potential biomarkers in diabetic retinopathy: integrated multi-omic analyses.Frontiers in endocrinology · 2025Review
- Proteomic Identification of Saliva Proteins as Noninvasive Diagnostic Biomarkers.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Changes in Tear Proteomic Profile in Ocular Diseases.International journal of environmental research and public health · 2022Review
- Relationship between serum and tear levels of tissue plasminogen activator and plasminogen activator inhibitor-1 in diabetic retinopathy.BMC ophthalmology · 2022Article
- Mass spectrometry-based tear proteomics for noninvasive biomarker discovery.Mass spectrometry reviews · 2022Review
- Colorimetric and Electrochemical Screening for Early Detection of Diabetes Mellitus and Diabetic Retinopathy-Application of Sensor Arrays and Machine Learning.Sensors (Basel, Switzerland) · 2022Review
- Heart, Eye, and Artificial Intelligence: A Review.Cardiology research · 2021Review
- Biomarkers for Progression in Diabetic Retinopathy: Expanding Personalized Medicine through Integration of AI with Electronic Health Records.Seminars in ophthalmology · 2021Article
- Proteomic Analysis of Tear Film Obtained from Diabetic Dogs.Animals : an open access journal from MDPI · 2020Article
- Functional Genomics of the Retina to Elucidate its Construction and Deconstruction.International journal of molecular sciences · 2019Review
- Proteomic Biomarkers of Retinal Inflammation in Diabetic Retinopathy.International journal of molecular sciences · 2019Review
- Noninvasive diagnostic methods for diabetes mellitus from tear fluid.RSC advances · 2019Article
- Computer aided diabetic retinopathy detection based on ophthalmic photography: a systematic review and Meta-analysis.International journal of ophthalmology · 2019Article
- Discovery of tear biomarkers in children with chronic non-infectious anterior uveitis: a pilot study.Journal of ophthalmic inflammation and infection · 2018Article
- Comparison of tear proteome in allergic rhinoconjunctivitis patients and controls with respect to pollen season.Allergy · 2018Article
- Quantitative proteomics analysis of vitreous body from type 2 diabetic patients with proliferative diabetic retinopathy.BMC ophthalmology · 2018Observational
- Machine Learning and Data Mining Methods in Diabetes Research.Computational and structural biotechnology journal · 2017Review
- The Role of Retinal Imaging and Portable Screening Devices in Tele-ophthalmology Applications for Diabetic Retinopathy Management.Current diabetes reports · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background. It is estimated that 347 million people suffer from diabetes mellitus (DM), and almost 5 million are blind due to diabetic retinopathy (DR). The progression of DR can be slowed down with early diagnosis and treatment. Therefore our aim was to develop a novel automated method for DR screening. Methods. 52 patients with diabetes mellitus were enrolled into the project. Of all patients, 39 had signs of DR. Digital retina images and tear fluid samples were taken from each eye. The results from the tear fluid proteomics analysis and from digital microaneurysm (MA) detection on fundus images were used as the input of a machine learning system. Results. MA detection method alone resulted in 0.84 sensitivity and 0.81 specificity. Using the proteomics data for analysis 0.87 sensitivity and 0.68 specificity values were achieved. The combined data analysis integrated the features of the proteomics data along with the number of detected MAs in the associated image and achieved sensitivity/specificity values of 0.93/0.78. Conclusions. As the two different types of data represent independent and complementary information on the outcome, the combined model resulted in a reliable screening method that is comparable to the requirements of DR screening programs applied in clinical routine.
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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.