ArticleAnnals of translational medicine2021
Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning.
Article in Annals of translational medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 39 citations in OpenAlex.
- Multimodal deep learning prediction of treatment response to anti-vascular endothelial growth factor in diabetic macular oedema.Eye (London, England) · 2026Article
- The evolving role of artificial intelligence in optimizing treatment and patient selection in diabetic macular edema.Indian journal of ophthalmology · 2026Review
- DME-RWKV: An Interpretable Multimodal Deep Learning Framework for Predicting Anti-VEGF Response in Diabetic Macular Edema.Bioengineering (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Diabetic Retinopathy and Diabetic Macular Edema: A Narrative Review.Bioengineering (Basel, Switzerland) · 2025Review
- Nonadherence to anti-VEGF intravitreal injections in patients with diabetic macular edema : The names of the authors.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Review
- 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
- Prediction of long-term uncorrected distance visual acuity in surgically SMILE corrected myopic eyes using machine learning.BMJ open ophthalmology · 2025Article
- Precision Medicine for Diabetic Retinopathy: Integrating Genetics, Biomarkers, Lifestyle, and AI.Genes · 2025Review
- Ensemble machine learning algorithm for anti-VEGF treatment efficacy prediction in diabetic macular edema.BMC ophthalmology · 2025Article
- Artificial intelligence for early detection of diabetes mellitus complications via retinal imaging.Journal of diabetes and metabolic disorders · 2025Review
- Multimodal deep transfer learning to predict retinal vein occlusion macular edema recurrence after anti-VEGF therapy.Heliyon · 2024Article
- Applications of artificial intelligence and bioinformatics methodologies in the analysis of ocular biofluid markers: a scoping review.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- Review
- Estimation of Visual Function Using Deep Learning From Ultra-Widefield Fundus Images of Eyes With Retinitis Pigmentosa.JAMA ophthalmology · 2023Article
- Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images.Journal of clinical medicine · 2022Article
- Predicting Visual Improvement After Macular Hole Surgery: A Combined Model Using Deep Learning and Clinical Features.Translational vision science & technology · 2022Article
- The Fundus Structural and Functional Predictions of DME Patients After Anti-VEGF Treatments.Frontiers in endocrinology · 2022Article
- Prediction of Visual Acuity after anti-VEGF Therapy in Diabetic Macular Edema by Machine Learning.Journal of diabetes research · 2022Article
- Treatment of Macular Edema in Vascular Retinal Diseases: A 2021 Update.Journal of clinical medicine · 2021Review
- Narrative review of artificial intelligence in diabetic macular edema: Diagnosis and predicting treatment response using optical coherence tomography.Indian journal of ophthalmology · 2021Review
Corrections and comments
- Erratum issued
Authors and funding
18 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThis study aimed to predict the treatment outcomes in patients with diabetic macular edema (DME) after 3 monthly anti-vascular endothelial growth factor (VEGF) injections using machine learning (ML) based on pretreatment optical coherence tomography (OCT) images and clinical variables.
methodsAn ensemble ML system consisting of four deep learning (DL) models and five classical machine learning (CML) models was developed to predict the posttreatment central foveal thickness (CFT) and the best-corrected visual acuity (BCVA). A total of 363 OCT images and 7,587 clinical data records from 363 eyes were included in the training set (304 eyes) and external validation set (59 eyes). The DL models were trained using the OCT images, and the CML models were trained using the OCT images features and clinical variables. The predictive posttreatment CFT and BCVA values were compared with true outcomes obtained from the medical records.
resultsFor CFT prediction, the mean absolute error (MAE), root mean square error (RMSE), and R
conclusionsOur ensemble ML system accurately predicted posttreatment CFT and BCVA after anti-VEGF injections in DME patients, and can be used to prospectively assess the efficacy of anti-VEGF therapy in DME patients.
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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.