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

Le résumé fourni par la source

Purpose: To develop a multisource machine learning model for detecting referral-warranted retinopathy of prematurity (RW-ROP) using retinal images and demographics. Design: Secondary analysis of data from the Telemedicine Approaches to Evaluating Acute-Phase Retinopathy of Prematurity Study. Subjects: One thousand two hundred fifty-seven premature infants (mean birth weight 864 g; mean gestational age 27 weeks; 19.4% with RW-ROP) enrolled from 12 clinical centers in North America. Methods: A multisource ROPNet (MS-ROPNet) model that combines a VGG-Swin Transformer model to extract features from retinal images and a random forest model that captures patterns of demographics was developed using central-view retinal images from 7741 eye visits with concurrent clinical eye examinations and demographic characteristics (birth weight, gestational age, sex, ethnicity, and age at retinal image). The MS-ROPnet model was compared to several existing machine learning models for detecting RW-ROP. Main Outcome Measures: Model performance metrics including the area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), sensitivity, specificity, and accuracy from fivefold cross-validation, with RW-ROP diagnosed by certified ophthalmologists as the reference standard. Results: The MS-ROPNet achieved an AUROC of 95.0 ± 0.7% (mean ± standard deviation), AUPRC of 78.0 ± 2.8%, sensitivity of 81.5 ± 4.0%, specificity of 94.3 ± 1.4%, and accuracy of 93.0 ± 0.9% under the default cutoff of 0.50 in predicted probability. After adjusting the cutoff to achieve higher sensitivity, MS-ROPNet had 90% sensitivity and specificity of 84.8 ± 2.5% (using cutoff 0.3504), and 95% sensitivity with specificity of 72.8 ± 6.9% (using cutoff 0.2074), which outperformed both existing multisource models and the best single-source model by ≥3.7% in specificity at the same sensitivity. Conclusions: The MS-ROPNet achieved high performance in RW-ROP classification by effectively integrating retinal images with demographics, demonstrating its potential for accurate risk stratification of RW-ROP. Financial Disclosures: The authors have no proprietary or commercial interest in any materials discussed in this article.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.