Systematic Review of Studies Investigating Infant Feeding Difficulties: Focus on Machine Learning Applications.

INTRODUCTION: This study aims to systematically review studies using machine learning for infant feeding difficulties and identify the clinical applicability and limitations of such approaches. METHODS: A literature search was conducted in accordance with the PRISMA guidelines. Studies on the use of machine learning for infants with feeding difficulties were analyzed. RESULTS: We screened 1,104 studies, and 10 were eligible for inclusion. These studies applied machine learning to measure physiological signals during feeding, such as the number of sucking, swallowing and breathing events, to classify or predict feeding difficulties. The most common algorithms in the analyzed studies included support vector machines (SVMs), k-nearest neighbors (KNNs), and decision trees (DTs), which generally achieved high classification accuracy. DISCUSSION: Although all included studies used sensor-based data, only four directly applied machine learning models. Future research should expand the direct applications of machine learning, standardize measurements, and validate algorithms to improve clinical utility.