Comparison and analysis of deep learning models for discriminating longitudinal and oblique vaginal septa based on ultrasound imaging.

Journal: BMC medical imaging
PMID: 39716160

Abstract

BACKGROUND: The longitudinal vaginal septum and oblique vaginal septum are female müllerian duct anomalies that are relatively less diagnosed but severely fertility-threatening in clinical practice. Ultrasound imaging is commonly used to examine the two vaginal malformations, but in fact it's difficult to make an accurate differential diagnosis. This study is intended to assess the performance of multiple deep learning models based on ultrasonographic images for distinguishing longitudinal vaginal septum and oblique vaginal septum.

Authors

  • Xiangyu Wang
    Key Laboratory of Animal Genetics and Breeding and Reproduction of Ministry of Agriculture and Rural Affairs, Institute of Animal Science, Chinese Academy of Agricultural Sciences, Beijing, China.
  • Liang Wang
    Information Department, Dazhou Central Hospital, Dazhou 635000, China.
  • Xin Hou
    College of Natural Resources and Environment, Northwest A&F University, Yangling 712100, China.
  • Jingfang Li
    Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095 Jiefang Avenue, Wuhan, Hubei Province, 430030, China.
  • Jin Li
    Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.
  • Xiangyi Ma
    Department of Obstetrics and Gynecology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, No.1095 Jiefang Avenue, Wuhan, Hubei Province, 430030, China. xyma@tjh.tjmu.edu.cn.

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