ArticleScientific reports2022
Evaluation of machine learning algorithms for trabeculectomy outcome prediction in patients with glaucoma.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Artificial intelligence for surgical outcome prediction in glaucoma: a systematic review.Frontiers in big data · 2025Pooled it
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Artificial Intelligence for Predicting Postoperative Outcomes in Anterior Segment and Ocular Adnexa Surgery: A Systematic Review.Ophthalmology science · 2026Review
- Global research status in the treatment of glaucoma: a systematic bibliometric analysis.International journal of ophthalmology · 2026Article
- Predicting Intraocular Pressure From Glaucoma Patients Receiving Medication Treatment Using Explainable Machine Learning.BioMed research international · 2026Article
- An extensive analysis of machine learning techniques for identifying glaucoma.International ophthalmology · 2025Review
- Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting.BMC medical informatics and decision making · 2025Article
- Artificial Intelligence in Glaucoma: Advances in Diagnosis, Progression Forecasting, and Surgical Outcome Prediction.International journal of molecular sciences · 2025Review
- Advances in Glaucoma Diagnosis and Treatment: Integrating Innovations for Enhanced Patient Outcomes.Biomedicines · 2025Article
- Influence of Ocular and Systemic Variables on Intraocular Pressure Change Following Phacoemulsification in Non-Glaucomatous Eyes.Journal of ophthalmic & vision research · 2025Article
- A scoping review of advancements in machine learning for glaucoma: current trends and future direction.Frontiers in medicine · 2025Article
- Multicenter evaluation of machine and deep learning methods to predict glaucoma surgical outcomes.Frontiers in artificial intelligence · 2025Article
- Lactate-related gene signatures as prognostic predictors and comprehensive analysis of immune profiles in nasopharyngeal carcinoma.Journal of translational medicine · 2024Article
- Artificial Intelligence and Advanced Technology in Glaucoma: A Review.Journal of personalized medicine · 2024Review
- Evaluation of machine learning approach for surgical results of Ahmed valve implantation in patients with glaucoma.BMC ophthalmology · 2024Article
- Predicting Glaucoma Surgical Outcomes Using Neural Networks and Machine Learning on Electronic Health Records.Translational vision science & technology · 2024Article
- Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Artificial intelligence in the anterior segment of eye diseases.International journal of ophthalmology · 2024Review
- Anterior segment parameters after trabeculectomy in pseudoexfoliation glaucoma versus primary open-angle glaucoma.Medical hypothesis, discovery & innovation ophthalmology journal · 2024Article
- Machine learning regression algorithms to predict short-term efficacy after anti-VEGF treatment in diabetic macular edema based on real-world data.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
The purpose of this study was to evaluate the performance of machine learning algorithms to predict trabeculectomy surgical outcomes. Preoperative systemic, demographic and ocular data from consecutive trabeculectomy surgeries from a single academic institution between January 2014 and December 2018 were incorporated into models using random forest, support vector machine, artificial neural networks and multivariable logistic regression. Mean area under the receiver operating characteristic curve (AUC) and accuracy were used to evaluate the discrimination of each model to predict complete success of trabeculectomy surgery at 1 year. The top performing model was optimized using recursive feature selection and hyperparameter tuning. Calibration and net benefit of the final models were assessed. Among the 230 trabeculectomy surgeries performed on 184 patients, 104 (45.2%) were classified as complete success. Random forest was found to be the top performing model with an accuracy of 0.68 and AUC of 0.74 using 5-fold cross-validation to evaluate the final optimized model. These results provide evidence that machine learning models offer value in predicting trabeculectomy outcomes in patients with refractory glaucoma.
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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.