ArticleJournal of clinical medicine2018
A Novel Machine Learning Algorithm to Automatically Predict Visual Outcomes in Intravitreal Ranibizumab-Treated Patients with Diabetic Macular Edema.
Article in Journal of clinical medicine, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Artificial intelligence in the diagnosis and prognosis of ocular trauma: a systematic review.BMC ophthalmology · 2026Pooled it
- Comparison of Diagnosis Accuracy between a Backpropagation Artificial Neural Network Model and Linear Regression in Digestive Disease Patients: an Empirical Research.Computational and mathematical methods in medicine · 2021Pooled it
- Prediction of Long-Term Treatment Outcomes for Diabetic Macular Edema Using a Generative Adversarial Network.Translational vision science & technology · 2024Trial
- 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
- Early retinal changes in type 2 diabetes detected by texture-based OCT analysis: potential approach for subclinical diabetic retinopathy diagnosis.Eye and vision (London, England) · 2025Article
- Ensemble machine learning algorithm for anti-VEGF treatment efficacy prediction in diabetic macular edema.BMC ophthalmology · 2025Article
- Retinal OCT-Derived Texture Features as Potential Biomarkers for Early Diagnosis and Progression of Diabetic Retinopathy.Investigative ophthalmology & visual science · 2025Article
- Aflibercept or ranibizumab for diabetic macular edema.Medical hypothesis, discovery & innovation ophthalmology journal · 2024Article
- Prediction of postoperative visual acuity in patients with age-related cataracts using macular optical coherence tomography-based deep learning method.Frontiers in medicine · 2023Article
- Predicting visual acuity with machine learning in treated ocular trauma patients.International journal of ophthalmology · 2023Article
- Binary dose level classification of tumour microvascular response to radiotherapy using artificial intelligence analysis of optical coherence tomography images.Scientific reports · 2022Article
- Prediction of Visual Acuity after anti-VEGF Therapy in Diabetic Macular Edema by Machine Learning.Journal of diabetes research · 2022Article
- Narrative review of artificial intelligence in diabetic macular edema: Diagnosis and predicting treatment response using optical coherence tomography.Indian journal of ophthalmology · 2021Review
- Machine learning-based prediction of anatomical outcome after idiopathic macular hole surgery.Annals of translational medicine · 2021Article
- Automatic prediction of treatment outcomes in patients with diabetic macular edema using ensemble machine learning.Annals of translational medicine · 2021Article
- An Optical Coherence Tomography-Based Deep Learning Algorithm for Visual Acuity Prediction of Highly Myopic Eyes After Cataract Surgery.Frontiers in cell and developmental biology · 2021Article
- Applications of Artificial Intelligence for the Detection, Management, and Treatment of Diabetic Retinopathy.International ophthalmology clinics · 2020Article
- Artificial intelligence in ophthalmology: Is it just hype with no substance or the real McCoy.Indian journal of ophthalmology · 2019Article
- Predictors of limited early response to anti-vascular endothelial growth factor therapy in neovascular age-related macular degeneration with machine learning feature importance.Saudi journal of ophthalmology : official journal of the Saudi Ophthalmological SocietyArticle
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeArtificial neural networks (ANNs) are one type of artificial intelligence. Here, we use an ANN-based machine learning algorithm to automatically predict visual outcomes after ranibizumab treatment in diabetic macular edema.
methodsPatient data were used to optimize ANNs for regression calculation. The target was established as the final visual acuity at 52, 78, or 104 weeks. The input baseline variables were sex, age, diabetes type or condition, systemic diseases, eye status and treatment time tables. Three groups were randomly devised to build, test and demonstrate the accuracy of the algorithms.
resultsAt 52, 78 and 104 weeks, 512, 483 and 464 eyes were included, respectively. For the training group, testing group and validation group, the respective correlation coefficients were 0.75, 0.77 and 0.70 (52 weeks); 0.79, 0.80 and 0.55 (78 weeks); and 0.83, 0.47 and 0.81 (104 weeks), while the mean standard errors of final visual acuity were 6.50, 6.11 and 6.40 (52 weeks); 5.91, 5.83 and 7.59; (78 weeks); and 5.39, 8.70 and 6.81 (104 weeks).
conclusionsMachine learning had good correlation coefficients for predicating prognosis with ranibizumab with just baseline characteristics. These models could be the useful clinical tools for prediction of success of the treatments.
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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.