分子影像学杂志2025，Vol.48Issue(10)：1213-1218,6.DOI:10.12122/j.issn.1674-4500.2025.10.04
基于MRI影像组学的机器学习模型对新生儿急性胆红素脑病具有较高诊断价值
Machine learning models based on MRI radiomics have high diagnostic value for neonatal acute bilirubin encephalopathy
摘要
Abstract
Objective To develop an efficient and robust machine learning model for predicting neonatal acute bilirubin encephalopathy based on T1WI radiomics features using six algorithms:Support Vector Machine(SVM),Logistic Regression,Random Forest,K-Nearest Neighbors,Naive Bayes,and Multilayer Perceptron.Methods A retrospective analysis was conducted involving 54 neonates clinically diagnosed with ABE admitted to the First Affiliated Hospital of Xinjiang Medical University from January 2019 to August 2023,with a mean gestational age of 37+2 to 40+1(38.03±2.57)weeks.Additionally,47 healthy neonates were selected as controls,with a mean gestational age of 37+4 to 40+5(38.05±2.61)weeks.High-throughput radiomics features were extracted from T1WI images using Python and Pyradiomics software.Feature selection was performed using Pearson correlation coefficients and least absolute shrinkage and selection operator(LASSO)regression.Subsequently,machine learning models were constructed based on the selected radiomics features,and the classification performance of each algorithm was compared.Results After feature extraction and selection,eight representative radiomics features were identified to construct the ABE radiomics prediction model.Among the algorithms tested,SVM achieved the highest accuracy of 0.739,surpassing the performance of the other five methods.Conclusion Machine learning models based on MRI radiomics show significant clinical potential for diagnosing neonatal ABE.Particularly,the SVM algorithm demonstrates superior classification performance and model stability,offering a novel approach to early ABE diagnosis with promising clinical application prospect.关键词
急性胆红素脑病/苍白球/磁共振成像/机器学习Key words
acute bilirubin encephalopathy/globus pallidus/magnetic resonance imaging/machine learning引用本文复制引用
董双君,孙彬,吴淼,贾文霄..基于MRI影像组学的机器学习模型对新生儿急性胆红素脑病具有较高诊断价值[J].分子影像学杂志,2025,48(10):1213-1218,6.基金项目
新疆维吾尔自治区自然科学基金(2022D01C436) （2022D01C436）
新疆青年科技英才项目(2023TSYCCX0067) （2023TSYCCX0067）
