Development and validation of a cranial ultrasound imaging-based deep learning model for periventricular-intraventricular haemorrhage detection and grading: a two-centre study.

Journal: Pediatric radiology
Published Date: Jul 29, 2025

Abstract

BACKGROUND: Periventricular-intraventricular haemorrhage (IVH) is the most prevalent type of neonatal intracranial haemorrhage. It is especially threatening to preterm infants, in whom it is associated with significant morbidity and mortality. Cranial ultrasound has become an important means of screening periventricular IVH in infants. The integration of artificial intelligence with neonatal ultrasound is promising for enhancing diagnostic accuracy, reducing physician workload, and consequently improving periventricular IVH outcomes.

Authors

  • Yahui Peng
  • Zhensheng Hu
  • Mianmian Wen
    Department of Comprehensive traditional Chinese medicine, Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, China.
  • Yishu Deng
    Artificial Intelligence Laboratory, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, China.
  • Dan Zhao
    Key Laboratory of Hunan Province for Water Environment and Agriculture Product Safety, College of Chemistry and Chemical Engineering, Central South University, Changsha, 410083, China.
  • Yuwei Yu
    Department of Medical equipment department, The Third Affiliated Hospital, Guangzhou Medical University, GuangZhou, China.
  • Weixiang Liang
    Department of Ultrasound Medicine; Laboratory of Ultrasound Molecular Imaging; Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; The Third Affiliated Hospital, Guangzhou Medical University, No. 63, Duobao Road, Liwan District, Guangzhou, China. 13503089717@163.com.
  • Xianhua Dai
  • Yi Wang
    Department of Neurology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.

Keywords

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