Magnetic resonance imaging features to evaluate the neonatal hypoglycemia brain injury and investigation of related risk factors under the fuzzy C‐means clustering intelligent algorithm

• Published in: Expert systems 2024-07, Vol.41 (7), p.n/a
• Main Authors: Jin, Dongmei, Zhang, Zhongxu, Ma, Yanru, Dai, Zhushan, Zhao, Lili, Ma, Tongyao, Chen, Guoping
• Format: Article
• Language: English
• Subjects: Algorithms; Clustering; Factor analysis; FCM intelligent algorithm; Glucose; Head injuries; high risk factors; Hypoglycemia; hypoglycemia brain injury; Image quality; Image segmentation; Magnetic resonance imaging; Medical imaging; Risk factors; Traumatic brain injury
• ISSN: 0266-4720; 1468-0394
• DOI: 10.1111/exsy.13491

This research was aimed to analyse the application value of magnetic resonance imaging based on Fuzzy C‐means (FCM) algorithm in neonatal hypoglycemia brain injury (HBI), and explore the risk factors related to the occurrence of brain injury in children, to provide guidance for clinical diagnosis and treatment. 114 children with hypoglycemia were divided into brain injury group (58 cases) and no brain injury group (56 cases) according to whether they had brain injury or not. The MRI image signal performance, general data, average minimum blood glucose value, duration of hypoglycemia, first feeding time, age of onset of hypoglycemia, and algorithm segmentation performance of the two groups of patients were observed and compared. Furthermore, the Logistic factor analysis was carried out to summarize the MRI characteristics and related risk factors of neonatal hypoglycemic brain injury. The results showed that the average minimum blood glucose (1.09 ± 0.53 mmoL/L) in the brain injury group was lower than that in the non‐brain injury group (1.75 ± 0.49 mmoL/L), and the duration of hypoglycemia (43.1 ± 21.07 h) was higher than that in the non‐brain injury group (13.79 ± 6.81 h), p