Deep learning model for classifying endometrial lesions.

Journal: Journal of translational medicine
PMID: 33407588

Abstract

BACKGROUND: Hysteroscopy is a commonly used technique for diagnosing endometrial lesions. It is essential to develop an objective model to aid clinicians in lesion diagnosis, as each type of lesion has a distinct treatment, and judgments of hysteroscopists are relatively subjective. This study constructs a convolutional neural network model that can automatically classify endometrial lesions using hysteroscopic images as input.

Authors

  • YunZheng Zhang
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, 39 Huaxiang Road, Shenyang, 110021, People's Republic of China.
  • Zihao Wang
    Department of Neurosurgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
  • Jin Zhang
    Department of Otolaryngology, The Second People's Hospital of Yibin, Yibin, Sichuan, China.
  • CuiCui Wang
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, 39 Huaxiang Road, Shenyang, 110021, People's Republic of China.
  • YuShan Wang
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, 39 Huaxiang Road, Shenyang, 110021, People's Republic of China.
  • Hao Chen
    The First School of Medicine, Wenzhou Medical University, Wenzhou, China.
  • LuHe Shan
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, 39 Huaxiang Road, Shenyang, 110021, People's Republic of China.
  • JiaNing Huo
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, 39 Huaxiang Road, Shenyang, 110021, People's Republic of China.
  • Jiahui Gu
    Research Center for Analytical Sciences, Department of Chemistry, College of Sciences, Northeastern University, Shenyang, Liaoning Province 110819, China.
  • Xiaoxin Ma
    Department of Obstetrics and Gynecology, Shengjing Hospital of China Medical University, 39 Huaxiang Road, Shenyang, 110021, People's Republic of China. maxx@sj-hospital.org.

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