ArticleScientific reports2023
Machine learning regression algorithms to predict short-term efficacy after anti-VEGF treatment in diabetic macular edema based on real-world data.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Prediction of Long-Term Treatment Outcomes for Diabetic Macular Edema Using a Generative Adversarial Network.Translational vision science & technology · 2024Trial
- The evolving role of artificial intelligence in optimizing treatment and patient selection in diabetic macular edema.Indian journal of ophthalmology · 2026Review
- Impact of Comorbid Hypertension and Diabetes on Anti-VEGF Treatment Outcomes in Macular Edema.Journal of ophthalmology · 2026Article
- Diagnostic Accuracy of Artificial Intelligence in Predicting Anti-VEGF Treatment Response in Diabetic Macular Edema: A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2025Review
- Opportunities for AI-based Model-informed Drug Development: A Comparative Analysis of NONMEM and AI-based Models for Population Pharmacokinetic Prediction.The AAPS journal · 2025Article
- Application of deep learning algorithm for judicious use of anti-VEGF in diabetic macular edema.Scientific reports · 2025Article
- Artificial intelligence in proliferative diabetic retinopathy: advancing diagnosis, precision surgery, and anti-VEGF therapy optimization.Frontiers in medicine · 2025Review
- Development and validation of CNN-MLP models for predicting anti-VEGF therapy outcomes in diabetic macular edema.Scientific reports · 2024Article
- Development of sensor system and data analytic framework for non-invasive blood glucose prediction.Scientific reports · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The objective of this retrospective study was to predict short-term efficacy of anti-vascular endothelial growth factor (VEGF) treatment in diabetic macular edema (DME) using machine learning regression models. Real-world data from 279 DME patients who received anti-VEGF treatment at Ineye Hospital of Chengdu University of TCM between April 2017 and November 2022 were analyzed. Eight machine learning regression models were established to predict four clinical efficacy indicators. The accuracy of the models was evaluated using mean absolute error (MAE), mean square error (MSE) and coefficient of determination score (R
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