ArticleScientific reports2021
Predicting intraocular pressure using systemic variables or fundus photography with deep learning in a health examination cohort.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 23 citations in OpenAlex.
- Roadmap on advances in visual and physiological optics.Journal of optics (2010) · 2025Review
- Article
- Predicting renal function using fundus photography: role of confounders.The Korean journal of internal medicine · 2025Article
- Ensemble probabilistic quantization encoding for information preservation of numerical variables in convolutional neural networks.Scientific reports · 2025Article
- Influence of meteorological factors on intraocular pressure variability using a large-scale cohort.Scientific reports · 2024Article
- Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era.Medical & biological engineering & computing · 2024Article
- Deceptive Tricks in Artificial Intelligence: Adversarial Attacks in Ophthalmology.Journal of clinical medicine · 2023Review
- The causal relationship between COVID-19 and increased intraocular pressure: A bidirectional two-sample Mendelian randomization study.Frontiers in public health · 2023Article
- Linear association between aging and decreased blood thiol antioxidant activity in patients with cataract.Journal of clinical biochemistry and nutrition · 2023Article
- Application of a deep learning system in glaucoma screening and further classification with colour fundus photographs: a case control study.BMC ophthalmology · 2022Article
- Machine learning predicting myopic regression after corneal refractive surgery using preoperative data and fundus photography.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The purpose of the current study was to predict intraocular pressure (IOP) using color fundus photography with a deep learning (DL) model, or, systemic variables with a multivariate linear regression model (MLM), along with least absolute shrinkage and selection operator regression (LASSO), support vector machine (SVM), and Random Forest: (RF). Training dataset included 3883 examinations from 3883 eyes of 1945 subjects and testing dataset 289 examinations from 289 eyes from 146 subjects. With the training dataset, MLM was constructed to predict IOP using 35 systemic variables and 25 blood measurements. A DL model was developed to predict IOP from color fundus photographs. The prediction accuracy of each model was evaluated through the absolute error and the marginal R-squared (mR
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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.