BACKGROUND AND OBJECTIVES Outcome prediction of preterm birth is important for neonatal care, yet prediction performance using conventional statistical models remains insufficient. Machine learning has a high potential for complex outcome prediction. In this scoping review, we provide an overview of the current applications of machine learning models in the prediction of neurodevelopmental outcomes in preterm infants, assess the quality of the developed models, and provide guidance for future application of machine learning models to predict neurodevelopmental outcomes of preterm infants. METHODS A systematic search was performed using PubMed. Studies were included if they reported on neurodevelopmental outcome prediction in preterm infants using predictors from the neonatal period and applying machine learning techniques. Data extraction and quality assessment were independently performed by 2 reviewers. RESULTS Fourteen studies were included, focusing mainly on very or extreme preterm infants, predicting neurodevelopmental outcome before age 3 years, and mostly assessing outcomes using the Bayley Scales of Infant Development. Predictors were most often based on MRI. The most prevalent machine learning techniques included linear regression and neural networks. None of the studies met all newly developed quality assessment criteria. Studies least prone to inflated performance showed promising results, with areas under the curve up to 0.86 for classification and R2 values up to 91% in continuous prediction. A limitation was that only 1 data source was used for the literature search. CONCLUSIONS Studies least prone to inflated prediction results are the most promising. The provided evaluation framework may contribute to improved quality of future machine learning models.

中文翻译：

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早产后神经发育结果的机器学习预测模型：范围审查和新的机器学习评估框架。

背景和目标 早产的结果预测对于新生儿护理很重要，但使用传统统计模型的预测性能仍然不足。机器学习在复杂的结果预测方面具有很高的潜力。在本次范围审查中，我们概述了机器学习模型在预测早产儿神经发育结果中的当前应用，评估所开发模型的质量，并为未来应用机器学习模型预测早产儿的神经发育结果提供指导。早产儿。方法 使用 PubMed 进行系统搜索。如果他们使用新生儿期的预测因子并应用机器学习技术报告早产儿的神经发育结果预测，则纳入研究。数据提取和质量评估由 2 名评审员独立进行。结果 纳入了 14 项研究，主要关注极早产儿或极早产儿，预测 3 岁前的神经发育结果，并主要使用贝利婴儿发育量表评估结果。预测因素最常基于 MRI。最流行的机器学习技术包括线性回归和神经网络。没有一项研究符合所有新制定的质量评估标准。最不容易出现夸大性能的研究显示出有希望的结果，分类的曲线下面积高达 0.86，连续预测的 R2 值高达 91%。一个限制是仅使用 1 个数据源进行文献检索。结论 最不容易出现夸大预测结果的研究是最有希望的。所提供的评估框架可能有助于提高未来机器学习模型的质量。