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Machine Learning Models for Predicting Treatment-Requiring Retinopathy of Prematurity in the e-ROP Study
Dinglun He1, Xinwei Luo1, Bowen Ying2
1Department of Computer Science and Engineering, Lehigh University, Bethlehem, Pennsylvania, USA.
Insights
Machine learning models moderately predict treatment-requiring retinopathy of prematurity (TR-ROP) using early imaging and clinical data. Deep neural networks showed the highest accuracy, offering potential for early identification of high-risk infants.
Area of Science:
- Ophthalmology
- Medical Imaging
- Machine Learning
Background:
- Retinopathy of prematurity (ROP) is a leading cause of vision loss in premature infants.
- Early detection and treatment of treatment-requiring ROP (TR-ROP) are crucial to prevent severe visual impairment.
- Current screening methods rely on manual examination, which can be resource-intensive.
Purpose of the Study:
- To evaluate machine learning (ML) models for predicting TR-ROP.
- To assess the utility of image findings at 32-34 weeks postmenstrual age, along with demographic and clinical data, for TR-ROP prediction.
Main Methods:
- A secondary analysis of 771 infants (birth weight < 1251 g) from the e-ROP study was performed.
- Six ML models (KNN, SVM, Random Forest, XGBoost, DNN, Transformer) were trained and evaluated.
- Performance metrics included AUC, accuracy, sensitivity, and specificity.
Main Results:
- ML models incorporating image findings and clinical data achieved AUCs from 0.777 to 0.853.
- The Deep Neural Network (DNN) model demonstrated the highest performance, with an AUC of 0.853, sensitivity of 0.929, and specificity of 0.644.
- Using image findings alone, the DNN achieved an AUC of 0.787.
Conclusions:
- ML models show moderate predictive capability for TR-ROP.
- DNN models offer promising results for early TR-ROP risk identification.
- Further research is needed to enhance ML model performance for clinical application.
Purpose:
To evaluate machine learning (ML) models for predicting treatment-requiring retinopathy of prematurity (TR-ROP) using image findings at 32 to 34 weeks of postmenstrual age, along with demographic and clinical characteristics.
Methods:
This secondary analysis included 771 infants with a birth weight of less than 1251 g who had at least one imaging session by 34 weeks postmenstrual age and at least one subsequent ROP examination for determining TR-ROP by ophthalmologists in the Telemedicine Approaches to Evaluating Acute-Phase Retinopathy of Prematurity (e-ROP) Study. Six ML models (K-nearest neighbors, support vector machine, random forest, extreme gradient boosting, deep neural network [DNN], and transformer) were evaluated for predicting TR-ROP. Prediction performance was evaluated using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.
Results:
Using image findings and demographic and clinical data, ML models achieved AUCs ranging from 0.777 (K-nearest neighbors) to 0.853 (DNN), sensitivity ranging from 0.765 (extreme gradient boosting) to 0.929 (DNN), and specificity ranging from 0.644 (DNN) to 0.698 (transformer). Using image findings alone, the DNN performed best with an AUC of 0.787, sensitivity of 0.729, and specificity of 0.725.
Conclusions:
ML models using image findings, demographics and clinical characteristics moderately predict TR-ROP, with DNN achieving the highest AUC and sensitivity. Although ML models may provide tools for the early identification of high-risk infants for close monitoring and timely treatment of TR-ROP, future research is needed to improve their performance.
Translational Relevance:
ML has the potential to predict TR-ROP risk based on early image findings, demographics, and clinical characteristics.
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