Biomedical Engineering, Kyung Hee University, Yongin-si, Republic of Korea.
Department of Pediatrics, Kyung Hee University Medical Center, Kyung Hee University College of Medicine, Seoul, Republic of Korea.
J Med Internet Res. 2023 Jul 10;25:e47612. doi: 10.2196/47612.
Respiratory distress syndrome (RDS) is a disease that commonly affects premature infants whose lungs are not fully developed. RDS results from a lack of surfactant in the lungs. The more premature the infant is, the greater is the likelihood of having RDS. However, even though not all premature infants have RDS, preemptive treatment with artificial pulmonary surfactant is administered in most cases.
We aimed to develop an artificial intelligence model to predict RDS in premature infants to avoid unnecessary treatment.
In this study, 13,087 very low birth weight infants who were newborns weighing less than 1500 grams were assessed in 76 hospitals of the Korean Neonatal Network. To predict RDS in very low birth weight infants, we used basic infant information, maternity history, pregnancy/birth process, family history, resuscitation procedure, and test results at birth such as blood gas analysis and Apgar score. The prediction performances of 7 different machine learning models were compared, and a 5-layer deep neural network was proposed in order to enhance the prediction performance from the selected features. An ensemble approach combining multiple models from the 5-fold cross-validation was subsequently developed.
Our proposed ensemble 5-layer deep neural network consisting of the top 20 features provided high sensitivity (83.03%), specificity (87.50%), accuracy (84.07%), balanced accuracy (85.26%), and area under the curve (0.9187). Based on the model that we developed, a public web application that enables easy access for the prediction of RDS in premature infants was deployed.
Our artificial intelligence model may be useful for preparations for neonatal resuscitation, particularly in cases involving the delivery of very low birth weight infants, as it can aid in predicting the likelihood of RDS and inform decisions regarding the administration of surfactant.
呼吸窘迫综合征（RDS）是一种常见于肺部未完全发育的早产儿的疾病。RDS 是由于肺部缺乏表面活性剂引起的。早产儿越早产，患 RDS 的可能性就越大。然而，即使不是所有的早产儿都患有 RDS，大多数情况下都会预防性地用人工肺表面活性剂进行治疗。
我们旨在开发一种人工智能模型来预测早产儿的 RDS，以避免不必要的治疗。
在这项研究中，我们评估了韩国新生儿网络的 76 家医院的 13087 名极低出生体重儿。为了预测极低出生体重儿的 RDS，我们使用了基本的婴儿信息、产科史、妊娠/分娩过程、家族史、复苏程序以及出生时的血气分析和 Apgar 评分等测试结果。比较了 7 种不同机器学习模型的预测性能，并提出了一个 5 层深度神经网络，以从选定的特征中提高预测性能。随后，开发了一种结合 5 折交叉验证中多个模型的集成方法。
我们提出的由前 20 个特征组成的集成 5 层深度神经网络具有较高的敏感性（83.03%）、特异性（87.50%）、准确性（84.07%）、平衡准确性（85.26%）和曲线下面积（0.9187）。基于我们开发的模型，我们部署了一个公共的网络应用程序，方便了早产儿 RDS 的预测。
我们的人工智能模型可能有助于新生儿复苏的准备工作，特别是在极低出生体重儿分娩的情况下，因为它可以帮助预测 RDS 的可能性，并为表面活性剂的使用提供决策依据。
