Multisource Machine Learning Model for Detecting Referral-Warranted Retinopathy of Prematurity

Area of Science:

• Ophthalmology
• Medical Artificial Intelligence
• Neonatology

Background:

• Retinopathy of prematurity (ROP) is a leading cause of visual impairment in premature infants.
• Early detection of referral-warranted ROP (RW-ROP) is crucial for timely intervention and preventing vision loss.
• Current diagnostic methods can be resource-intensive and require specialized expertise.

Purpose of the Study:

• To develop and evaluate a multisource machine learning model (MS-ROPNet) for detecting RW-ROP.
• To integrate retinal imaging data with infant demographic information for enhanced detection accuracy.

Main Methods:

• A secondary analysis of data from the Telemedicine Approaches to Evaluating Acute-Phase Retinopathy of Prematurity Study was performed.
• The MS-ROPNet model combined a VGG-Swin Transformer for image feature extraction and a random forest for demographic pattern analysis.
• The model was trained and validated using retinal images and demographic data from 1,257 premature infants across 12 clinical centers.

Main Results:

• The MS-ROPNet achieved high performance metrics, including an Area Under the Receiver Operating Characteristic Curve (AUROC) of 95.0 ± 0.7%.
• The model demonstrated excellent sensitivity and specificity, with adjustable cutoffs to prioritize either sensitivity or specificity.
• MS-ROPNet outperformed existing multisource and single-source models in detecting RW-ROP.

Conclusions:

• The MS-ROPNet model effectively integrates retinal images and demographic data for accurate RW-ROP classification.
• This AI-driven approach shows significant potential for precise risk stratification of RW-ROP in premature infants.
• The findings support the clinical utility of advanced machine learning in managing ROP.