Predictive value of ultrasonic artificial intelligence in placental characteristics of early pregnancy for gestational diabetes mellitus.

Journal: Frontiers in endocrinology
PMID: 38544693

Abstract

BACKGROUND: To explore the predictive value of placental features in early pregnancy for gestational diabetes mellitus (GDM) using deep and radiomics-based machine learning (ML) applied to ultrasound imaging (USI), and to develop a nomogram in conjunction with clinical features.

Authors

  • Huien Zhou
    Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), GuangZhou, China.
  • Wanming Chen
    Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), GuangZhou, China.
  • Chen Chen
    The George Institute for Global Health, Faculty of Medicine, University of New South Wales, Sydney, NSW, Australia.
  • Yanying Zeng
    Department of Ultrasound, Tianhe District Maternal and Child Hospital of Guangzhou, GuangZhou, China.
  • Jialin Chen
    Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), GuangZhou, China.
  • Jianru Lin
    Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), GuangZhou, China.
  • Kun He
    1 Department of Hepatobiliary Surgery, The Second Affiliated Hospital of Chongqing Medical University , Chongqing, China .
  • Xinmin Guo
    Department of Ultrasound, Guangzhou Red Cross Hospital (Guangzhou Red Cross Hospital of Jinan University), GuangZhou, China.

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