Ensemble machine learning algorithm for anti-VEGF treatment efficacy prediction in diabetic macular edema

BMC Ophthalmol. 2025 Jul 1;25(1):352. doi: 10.1186/s12886-025-04181-x.

Abstract

Background: Diabetic macular edema (DME) is a leading cause of vision loss in diabetes, with variable responses to anti-vascular endothelial growth factor (anti-VEGF) therapy in DME patients. Current diagnosis relies on optical coherence tomography (OCT) imaging, but manual interpretation is limited. This study aims to integrate 3D-OCT features and clinical variables to develop machine learning (ML) models for predicting anti-VEGF treatment outcomes.

Methods and analysis: Medical records and 3D-OCT images of DME patients were included in this study. The 3D-OCT images were categorized into good and poor visual response groups based on the best corrected visual acuity at one month after three consecutive anti-VEGF treatments. The images and clinical features were subjected to assessment by 11 automatic classification models for anti-VEGF treatment responses in DME patients. The top 3 performing models were selected to build an ensemble model, and evaluated in the test dataset.

Results: This study included 142 patients with 3D-OCT images of 170 eyes. A total of 20 image and clinical features were selected for the model construction and test in DME patients responded to anti-VEGF therapy. Adaptive boosting (AdaBoost), GradientBoosting, and light gradient boosting machine (LightGBM) exhibited better performances than the remaining 8 models. The ensemble model constructed achieved a sensitivity of 0.941, specificity of 0.882, and accuracy of 0.912 in the test dataset, with an area under the receiver operating characteristic curve of 0.976.

Conclusion: This study established an ensemble ML algorithm based on 3D-OCT images and clinical features for automatic detection of treatment responses to anti-VEGF treatment in DME patients to predict the efficacy of anti-VEGF treatment in DME patients and assist clinicians in optimal treatment decisions.

Keywords: Anti-VEGF treatment; Diabetic macular edema; Machine learning; Optical coherence tomography..

MeSH terms

  • Aged
  • Algorithms*
  • Angiogenesis Inhibitors* / therapeutic use
  • Bevacizumab* / therapeutic use
  • Diabetic Retinopathy* / complications
  • Diabetic Retinopathy* / diagnosis
  • Diabetic Retinopathy* / drug therapy
  • Female
  • Humans
  • Imaging, Three-Dimensional
  • Intravitreal Injections
  • Machine Learning*
  • Macular Edema* / diagnosis
  • Macular Edema* / diagnostic imaging
  • Macular Edema* / drug therapy
  • Macular Edema* / etiology
  • Male
  • Middle Aged
  • ROC Curve
  • Ranibizumab / therapeutic use
  • Retrospective Studies
  • Tomography, Optical Coherence / methods
  • Treatment Outcome
  • Vascular Endothelial Growth Factor A* / antagonists & inhibitors
  • Visual Acuity

Substances

  • Angiogenesis Inhibitors
  • Vascular Endothelial Growth Factor A
  • Ranibizumab
  • Bevacizumab