ArticleScientific reports2020
Predictive modeling of proliferative vitreoretinopathy using automated machine learning by ophthalmologists without coding experience.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Risk factors for proliferative vitreoretinopathy after retinal detachment surgery: A systematic review and meta-analysis.PloS one · 2023Pooled it
- A Disorder-Aware Computational Framework to Identify Structurally Tractable Targets in Proliferative Vitreoretinopathy.Ophthalmology science · 2026Article
- Artificial Intelligence in Vitreoretinal Surgery: A Systematic Review of Current Applications and Future Directions.Ophthalmology and therapy · 2026Review
- Utilization of an automated machine learning approach for the detection of granular corneal dystrophy via slit lamp photographs.BMC ophthalmology · 2025Article
- Code-Free Machine Learning for the Detection of Common Ophthalmic Diseases.Translational vision science & technology · 2025Article
- Development and validation of the Artificial Intelligence-Proliferative Vitreoretinopathy (AI-PVR) Insight system for deep learning-based diagnosis and postoperative risk prediction in proliferative vitreoretinopathy using multimodal fundus imaging.Quantitative imaging in medicine and surgery · 2025Article
- Comparing code-free and bespoke deep learning approaches in ophthalmology.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Review
- Automated machine learning model for fundus image classification by health-care professionals with no coding experience.Scientific reports · 2024Article
- Comparing code-free deep learning models to expert-designed models for detecting retinal diseases from optical coherence tomography.International journal of retina and vitreous · 2024Article
- A Prediction Nomogram for Recurrent Retinal Detachment.Risk management and healthcare policy · 2023Article
- Deep learning for ultra-widefield imaging: a scoping review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Article
- An emergency room influx and trauma cases prediction in a Brazilian ophthalmological hospital by an ophthalmologist without code experience.Arquivos brasileiros de oftalmologia · 2022Article
- Risk factors for epiretinal membrane formation and peeling following pars plana vitrectomy for primary rhegmatogenous retinal detachment, an OCT guided analysis.International journal of retina and vitreous · 2022Article
- Evaluating an automated machine learning model that predicts visual acuity outcomes in patients with neovascular age-related macular degeneration.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Article
- Development of a code-free machine learning model for the classification of cataract surgery phases.Scientific reports · 2022Article
- American Society of Retina Specialists Artificial Intelligence Task Force Report.Journal of vitreoretinal diseasesArticle
- Performance of Automated Machine Learning in Predicting Outcomes of Pneumatic Retinopexy.Ophthalmology scienceArticle
- Development and deployment of a smartphone application for diagnosing trachoma: Leveraging code-free deep learning and edge artificial intelligence.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
- Artificial intelligence and digital solutions for myopia.Taiwan journal of ophthalmologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to assess the feasibility of machine learning (ML) algorithm design to predict proliferative vitreoretinopathy (PVR) by ophthalmologists without coding experience using automated ML (AutoML). The study was a retrospective cohort study of 506 eyes who underwent pars plana vitrectomy for rhegmatogenous retinal detachment (RRD) by a single surgeon at a tertiary-care hospital between 2012 and 2019. Two ophthalmologists without coding experience used an interactive application in MATLAB to build and evaluate ML algorithms for the prediction of postoperative PVR using clinical data from the electronic health records. The clinical features associated with postoperative PVR were determined by univariate feature selection. The area under the curve (AUC) for predicting postoperative PVR was better for models that included pre-existing PVR as an input. The quadratic support vector machine (SVM) model built using all selected clinical features had an AUC of 0.90, a sensitivity of 63.0%, and a specificity of 97.8%. An optimized Naïve Bayes algorithm that did not include pre-existing PVR as an input feature had an AUC of 0.81, a sensitivity of 54.3%, and a specificity of 92.4%. In conclusion, the development of ML models for the prediction of PVR by ophthalmologists without coding experience is feasible. Input from a data scientist might still be needed to tackle class imbalance-a common challenge in ML classification using real-world clinical data.
Indexed as
Identifiers
What OpenQuestion holds
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.