Prediction Models for Retinopathy of Prematurity Using Nonimaging Machine Learning Approaches: A Regional Multicenter...

To develop nonimaging machine learning models using clinical data from the first screening to predict the occurrence of retinopathy of prematurity (ROP).

This multicenter regional study was conducted in Yamagata Prefecture, Japan.

We collected clinical data of neonates born between October 2016 and September 2018 and screened in 4 neonatal care units.

The 35 variables available at the first screening were used as possible predictors to develop a decision tree, a random forest, a gradient-boosted tree, a neural network, and a Naive Bayes model. Parameter tuning was performed using a 10-fold cross-validation. This process was repeated 200 times using different random seeds for data partitioning.

The target outcome was the final ROP outcome (i.e., the development of any stage of ROP during hospitalization).

Of the 215 neonates screened, 43 (20.0%) developed ROP. The median gestational age was 31.4 (interquartile range: 28.1-33.4) weeks, and the median birth weight was 1502 (interquartile range: 967-1823) g. The mean 200-iteration area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and specificity of the random forest model were 0.93 (95% confidence interval [CI] 0.83-0.99), 90.1% (95% CI 84.1-95.2), 95.7% (95% CI 88.2-100), and 66.0% (95% CI 41.7-91.7), respectively. The mean 200-iteration AUC-ROC, accuracy, sensitivity, and specificity of the Naive Bayes model were 0.94 (95% CI 0.86-0.99), 90.6% (95% CI 84.1-96.8), 94.6% (95% CI 86.3-100), and 73.6% (95% CI 50.0-91.7), respectively.

Nonimaging machine learning methods have shown high performance in predicting the occurrence of ROP. These models can be beneficial when a fundus camera cannot capture images due to eye opacity and for hospitals that lack pediatric fundus cameras.

The author(s) have no proprietary or commercial interest in any materials discussed in this article.