Current progress and future prospects of machine learning in the diagnosis of neonatal encephalopathy: a narrative review.

Background and Objective: Neonatal encephalopathy (NE) can cause permanent neurological damage in newborns. NE greatly increases the burden of care placed on families. It also places a tremendous economic strain on the social health system. Currently, NE is mostly diagnosed by imaging and blood gas analysis. However, current diagnostic methods mostly lag behind the disease, leading to a lag in medical interventions for NE. In recent years, machine learning (ML) techniques have been applied to medicine, including in the early diagnosis and screening of diseases. This study aimed to provide an overview of existing research on the application of ML to NE and to offer insights for future investigations. Methods: A full library search in fuzzy matching mode was performed to retrieve articles from the Web of Science database published between January 1, 2008, and August 31, 2024 using the following search strategy: (neonatal encephalopathy * machine learning) (where NE comprised all the relevant diseases, and ML comprised the main algorithms), and the key information was filtered. Key Content and Findings: A total of 159 documents were retrieved, and 23 relevant documents were identified based on the topic, keywords and content. The relevant content showed that the included articles on NE and ML had issues in terms of study standardization, dichotomous study outcomes, and clinical usefulness. Conclusions: To date, most studies on the application of ML to NE have not comprehensively considered the aspects of experimental design, data processing, model building, and evaluation. It is hoped that such models will provide effective decision-making tools for clinical practice in the future, and thus improve the healthy life span of newborns.