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

AI / informaticsOphthalmology science· Jul 2026

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

Xinwei Luo, Yong Chen, Bowen Ying … Lifang He

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Pediatric OphthalmologyAI / informaticsDiagnosisQ1IF 5.5

AI model accurately detects retinopathy of prematurity needing specialist referral

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

Xinwei Luo … Lifang He

Ophthalmology science · Jul 2026 · Q1 in Ophthalmology

Purpose

To develop a multisource machine learning model for detecting referral-warranted retinopathy of prematurity (referral-warranted retinopathy of prematurity) using retinal images and demographics.

Methods

SUBJECTS: One thousand two hundred fifty-seven premature infants (mean birth weight 864 g; mean gestational age 27 weeks; 19.4% with referral-warranted retinopathy of prematurity) enrolled from 12 clinical centers in North America.

95.0%

Results

95.0%

Model correctly distinguished infants needing referral from those who didn't, 95% of the time (AUROC).

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Conclusion · 1 of 2

The multisource ROPNet achieved high performance in referral-warranted retinopathy of prematurity classification by effectively integrating retinal images with demographics, demonstrating its potential for accurate risk stratification of referral-warranted retinopathy of prematurity.

Conclusion · 2 of 2

FINANCIAL

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

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

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