Related Experiment Video
Updated: May 8, 2026
Assessment of Vascular Regeneration in the CNS Using the Mouse Retina
Published on: June 23, 2014
Prediction of Reactivation After Antivascular Endothelial Growth Factor Monotherapy for Retinopathy of Prematurity:
Rong Wu1, Yu Zhang1, Peijie Huang2
1Department of Ophthalmology, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Insights
Predicting retinopathy of prematurity (ROP) reactivation after anti-VEGF treatment is crucial. A new fusion model accurately forecasts ROP reactivation, aiding in optimized infant treatment and screening strategies.
Area of Science:
- Ophthalmology
- Medical Artificial Intelligence
- Neonatal Care
Background:
- Retinopathy of prematurity (ROP) is a leading cause of preventable childhood blindness.
- Intravitreal anti-VEGF injections are vital for preventing vision loss but can lead to ROP reactivation.
- Accurate prediction of ROP reactivation is essential for effective treatment and management.
Purpose of the Study:
- To develop and validate machine learning models for predicting ROP reactivation post-anti-VEGF treatment.
- To compare the performance of conventional, deep learning, and fusion models in ROP reactivation prediction.
Main Methods:
- Recruited 239 infants with ROP undergoing anti-VEGF treatment from three hospitals.
- Constructed and evaluated conventional machine learning, deep learning, and fusion models.
- Assessed model performance using area under the curve (AUC), accuracy, sensitivity, and specificity.
Main Results:
- Out of 239 cases, 90 (37.66%) experienced ROP reactivation.
- Conventional ML models achieved AUCs of approximately 0.806.
- The fusion model demonstrated superior performance with an AUC of 0.822, sensitivity of 0.800, and specificity of 0.686 in testing.
Conclusions:
- Developed three predictive models for ROP reactivation after anti-VEGF therapy.
- The fusion model exhibited the best predictive performance.
- This model can enhance ROP treatment strategies and improve post-treatment screening plans for infants.
Background:
Retinopathy of prematurity (ROP) is the leading preventable cause of childhood blindness. A timely intravitreal injection of antivascular endothelial growth factor (anti-VEGF) is required to prevent retinal detachment with consequent vision impairment and loss. However, anti-VEGF has been reported to be associated with ROP reactivation. Therefore, an accurate prediction of reactivation after treatment is urgently needed.
Objective:
To develop and validate prediction models for reactivation after anti-VEGF intravitreal injection in infants with ROP using multimodal machine learning algorithms.
Methods:
Infants with ROP undergoing anti-VEGF treatment were recruited from 3 hospitals, and conventional machine learning, deep learning, and fusion models were constructed. The areas under the curve (AUCs), accuracy, sensitivity, and specificity were used to show the performances of the prediction models.
Results:
A total of 239 cases with anti-VEGF treatment were recruited, including 90 (37.66%) with reactivation and 149 (62.34%) nonreactivation cases. The AUCs for the conventional machine learning model were 0.806 and 0.805 in the internal validation and test groups, respectively. The average AUC, sensitivity, and specificity in the test for the deep learning model were 0.787, 0.800, and 0.570, respectively. The specificity, AUC, and sensitivity for the fusion model were 0.686, 0.822, and 0.800 in a test, separately.
Conclusions:
We constructed 3 prediction models for ROP reactivation. The fusion model achieved the best performance. Using this prediction model, we could optimize strategies for treating ROP in infants and develop better screening plans after treatment.
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