ArticleJournal of clinical medicine2022
Which Explanatory Variables Contribute to the Classification of Good Visual Acuity over Time in Patients with Branch Retinal Vein Occlusion with Macular Edema Using Machine Learning?
Article in Journal of clinical medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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
7 citing papers in PubMed, 10 citations in OpenAlex.
- Predicting Visual Acuity after Retinal Vein Occlusion Anti-VEGF Treatment: Development and Validation of an Interpretable Machine Learning Model.Journal of medical systems · 2025Article
- Radiomics Analysis Based on Optical Coherence Tomography to Prognose the Efficacy of Anti-VEGF Therapy of Retinal Vein Occlusion-Related Macular Edema.Investigative ophthalmology & visual science · 2025Article
- Association between time to treatment and outcome in branch retinal vein occlusion.International ophthalmology · 2024Article
- Preoperative OCT Characteristics Contributing to Prediction of Postoperative Visual Acuity in Eyes with Macular Hole.Journal of clinical medicine · 2024Article
- Visual acuity prediction on real-life patient data using a machine learning based multistage system.Scientific reports · 2024Article
- Construction and validation of risk prediction models for different subtypes of retinal vein occlusion.Advances in ophthalmology practice and researchArticle
- Multimodal data-driven approaches in retinal vein occlusion: A narrative review integrating machine learning and bioinformatics.Advances in ophthalmology practice and researchReview
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
This study's goal is to determine the accuracy of a linear classifier that predicts the prognosis of patients with macular edema (ME) due to a branch retinal vein occlusion during the maintenance phase of antivascular endothelial growth factor (anti-VEGF) therapy. The classifier was created using the clinical information and optical coherence tomographic (OCT) findings obtained up to the time of the first resolution of ME. In total, 66 eyes of 66 patients received an initial intravitreal injection of anti-VEGF followed by repeated injections with the pro re nata (PRN) regimen for 12 months. The patients were divided into two groups: those with and those without good vision during the PRN phase. The mean AUC of the classifier was 0.93, and the coefficients of the explanatory variables were: best-corrected visual acuity (BCVA) at baseline was 0.66, BCVA at first resolution of ME was 0.51, age was 0.21, the average brightness of the ellipsoid zone (EZ) was -0.12, the intactness of the external limiting membrane (ELM) was -0.14, the average brightness of the ELM was -0.17, the brightness value of EZ was -0.17, the area of the outer segments of the photoreceptors was -0.20, and the intactness of the EZ was -0.24. This algorithm predicted the prognosis over time for individual patients during the PRN phase.
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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.