Artificial intelligence in polycystic ovarian syndrome management: past, present, and future.

Abstract

Authors

• Jinyuan Wang
  National University of Singapore, Singapore, Singapore.
• Ruxin Chen
  Department of Gynecological Endocrinology, Jinan Maternity and Child Care Hospital Affiliated to Shandong First Medical University, Jinan, 250001, China.
• Haojun Long
  Department of Dermatology, The Second Affiliated Hospital of Kunming Medical University, Kunming, 650101, Yunnan, China.
• Junhui He
  Key Laboratory for Experimental Teratology of the Ministry of Education and Center for Experimental Nuclear Medicine, School of Basic Medical Sciences, Cheeloo College of Medicine, Shandong University, Jinan, 250012, Shandong, China.
• Masong Tang
  Department of Nuclear Medicine, Zhongshan Hospital, Fudan University, Shanghai, 200032, China.
• Mingxuan Su
  MicroPort(Shanghai) MedBot Co. Ltd, Shanghai, 200031.
• Renhe Deng
  Department of Plastic and Reconstructive Surgery, Third Xiangya Hospital, Central South University, Changsha, 410013, Hunan, China.
• Yuru Chen
  School of Sports Engineering, Beijing Sport University, Beijing, China.
• Rongqian Ni
  Department of Reproduction and Genetics, The First Affiliated Hospital of Kunming Medical University, 295 Xichang Road, Kunming, 650032, Yunnan Province, China.
• Shuhua Zhao
  Department of Obstetrics and Gynecology, Xijing Hospital, The Fourth Military Medical University, Shaanxi, China.
• Meng Rao
  Department of Reproduction and Genetics, The First Affiliated Hospital of Kunming Medical University, 295 Xichang Road, Kunming, 650032, Yunnan Province, China.
• Huawei Wang
  Guangdong Provincial Key Laboratory of Optical Information Materials and Technology & Institute of Electronic Paper Displays, South China Academy of Advanced Optoelectronics, South China Normal University, Guangzhou 510006, China.
• Li Tang
  School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China.

Keywords

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