Application of machine learning and artificial intelligence in the diagnosis and classification of polycystic ovarian syndrome: a systematic review.

Journal: Frontiers in endocrinology
Published Date: Sep 18, 2023

Abstract

INTRODUCTION: Polycystic Ovarian Syndrome (PCOS) is the most common endocrinopathy in women of reproductive age and remains widely underdiagnosed leading to significant morbidity. Artificial intelligence (AI) and machine learning (ML) hold promise in improving diagnostics. Thus, we performed a systematic review of literature to identify the utility of AI/ML in the diagnosis or classification of PCOS.

Authors

  • Francisco J Barrera
    Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, United States.
  • Ethan D L Brown
    Neurological Surgery, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, USA.
  • Amanda Rojo
    Plataforma INVEST Medicina, Universidad Autónoma de Nuevo León- Knowledge Education Research (UANL-KER), Unit Mayo Clinic (KER Unit Mexico), Universidad Autónoma de Nuevo León, Monterrey, Mexico.
  • Javier Obeso
    Plataforma INVEST Medicina, Universidad Autónoma de Nuevo León- Knowledge Education Research (UANL-KER), Unit Mayo Clinic (KER Unit Mexico), Universidad Autónoma de Nuevo León, Monterrey, Mexico.
  • Hiram Plata
    Plataforma INVEST Medicina, Universidad Autónoma de Nuevo León- Knowledge Education Research (UANL-KER), Unit Mayo Clinic (KER Unit Mexico), Universidad Autónoma de Nuevo León, Monterrey, Mexico.
  • Eddy P Lincango
    Knowledge and Evaluation Research Unit-Endocrinology (KER-Endo), Mayo Clinic, Rochester, MN, United States.
  • Nancy Terry
    Division of Library Services, Office of Research Services, National Institutes of Health, Bethesda, MD, United States.
  • René Rodríguez-Gutiérrez
    Plataforma INVEST Medicina, Universidad Autónoma de Nuevo León- Knowledge Education Research (UANL-KER), Unit Mayo Clinic (KER Unit Mexico), Universidad Autónoma de Nuevo León, Monterrey, Mexico.
  • Janet E Hall
    Reproductive Physiology and Pathophysiology Group, Clinical Research Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States.
  • Skand Shekhar
    Reproductive Physiology and Pathophysiology Group, Clinical Research Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Research Triangle Park, NC, United States.

Keywords

External Resources

Popular Topics
Recent Journals