Deep learning feature-based model on abdominal radiography outperforms experts for early necrotizing enterocolitis diagnosis in neonates.

Journal: European radiology
Published Date: Jan 13, 2026

Abstract

OBJECTIVES: Plain abdominal radiography is a widely used imaging modality for diagnosing neonatal necrotizing enterocolitis (NEC), but the characteristic features of stage I NEC are often subtle, making early diagnosis challenging. This study explores the application of deep learning (DL) models to assist in the early diagnosis of stage I NEC. MATERIALS AND METHODS: This retrospective study included 380 and 300 neonates who underwent abdominal radiography at two centers between June 2016 and December 2023. Neonates were grouped based on a diagnosis of stage I NEC. DL features were extracted from the radiographs using the DenseNet121 model, based on which radiomics models were constructed using logistic regression (LR) and random forest (RF) algorithms. Performance was evaluated through receiver operating characteristic (ROC) curves. Both the training and external validation cohorts were used to assess model accuracy in distinguishing stage I NEC. Additionally, a direct comparison with human expert diagnostic performance was conducted. RESULTS: In the training cohort, 25 DL features were selected for model development. The area under the ROC curve (AUC) for LR and RF models was 0.972 (95% CI: 0.956-0.988) and 0.961 (95% CI: 0.942-0.980), respectively. In the external validation cohort, the models demonstrated AUCs of 0.964 (95% CI: 0.943-0.986) and 0.951 (95% CI: 0.925-0.976), respectively. These models evidently outperformed human experts in diagnostic performance. CONCLUSION: The DL model based on plain abdominal radiography effectively identified stage I NEC in neonates. This approach offers a non-invasive method to enhance early NEC diagnosis and support clinical decision-making. KEY POINTS: QuestionDeep learning (DL) models applied to plain abdominal radiography can enhance the early diagnosis of stage I neonatal necrotizing enterocolitis (NEC). FindingsIn this retrospective study involving 680 neonates from two centers, DL-based radiomics models achieved much higher accuracy for diagnosing stage I NEC than human radiologists. Clinical relevanceDL models based on plain abdominal radiography have the ability to significantly improve the early identification of stage I NEC, offering a non-invasive tool to support radiologists in early diagnosis.

Authors

  • Yu Wu
    Key Laboratory of Pesticide and Chemical Biology of Ministry of Education, International Joint Research Center for Intelligent Biosensing Technology and Health, College of Chemistry, Central China Normal University, Wuhan, 430079, People's Republic of China.
  • Hao Yang
    College of Agricultural Science and Engineering, Hohai University, Nanjing 210098, China.
  • Xiaomei Luo
    Medical Imaging Center, First Affiliated Hospital of Jinan University, Guangzhou 510630, China.
  • Haige Zheng
    Department of Radiology, Women and Children's Medical Center Affiliated to Guangzhou Medical University, Guangzhou, Guangdong Provincial 510623, China (H.Z.).
  • Yan Zhou
    Department of Computer Science, University of Texas at Dallas, Richardson, Texas 75080, United States.
  • Chengyan Chen
    Department of Radiology, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
  • Rui Wang
    Department of Clinical Laboratory Medicine Center, Inner Mongolia Autonomous Region People's Hospital, Hohhot, Inner Mongolia, China.
  • Hongsheng Liu
    School of Life Science, Liaoning University, Shenyang, 110036, China. [email protected].
  • Liandong Zuo
    Department of Science, Education and Date Management, Guangzhou Women and Children's Medical Center, Guangzhou Medical University, Guangzhou, China.
  • Xiaochun Zhang
    Department of Cardiology, Zhongshan Hospital, Shanghai Institute of Cardiovascular Diseases, National Clinical Research Center for Interventional Medicine, Fudan University, Shanghai, 200032, China.
  • Kejian Wang
    Innovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese Medicine, Jinan, Shandong, 250300, China. [email protected].
  • Xuehua Peng
    From the Department of Radiology, Shenzhen Nanshan People's Hospital, Shenzhen University, Taoyuan Rd No. 89, Nanshan District, Shenzhen 518000, Guangdong, China (H.H., Z.D., Y.Q.); Medical AI Laboratory and Guangdong Key Laboratory of Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen University, Shenzhen, China (J.M., R.L., B.H.); Department of Medical Imaging, People's Hospital of Longhua, Shenzhen, Guangdong, China (X.P., Y.Z.); and Department of Radiology, Shenzhen People's Hospital, Shenzhen, Guangdong, China (D.Z., G.H.).

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