A systematic review on the use of artificial intelligence in gynecologic imaging - Background, state of the art, and future directions.

Journal: Gynecologic oncology
Published Date:

Abstract

OBJECTIVE: Machine learning, deep learning, and artificial intelligence (AI) are terms that have made their way into nearly all areas of medicine. In the case of medical imaging, these methods have become the state of the art in nearly all areas from image reconstruction to image processing and automated analysis. In contrast to other areas, such as brain and breast imaging, the impacts of AI have not been as strongly felt in gynecologic imaging. In this review article, we: (i) provide a background of clinically relevant AI concepts, (ii) describe methods and approaches in computer vision, and (iii) highlight prior work related to image classification tasks utilizing AI approaches in gynecologic imaging.

Authors

  • Pallabi Shrestha
    Department of Radiology, Mayo Clinic, Rochester, MN, United States of America.
  • Bhavya Poudyal
    Department of Radiology, Mayo Clinic, Rochester, MN, United States of America.
  • Sepideh Yadollahi
    Department of Radiology, Mayo Clinic, Rochester, MN, United States of America.
  • Darryl E Wright
    Department of Radiology, Mayo Clinic, Rochester, MN, United States of America.
  • Adriana V Gregory
    Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, United States of America.
  • Joshua D Warner
    Department of Radiology, Mayo Clinic, Rochester, MN, United States of America.
  • Panagiotis Korfiatis
    From the Department of Radiology, Mayo Clinic, 200 First St SW, Rochester, MN 55905.
  • Isabel C Green
    Department of Obstetrics and Gynecology, Mayo Clinic, Rochester, MN, United States of America.
  • Sarah L Rassier
    Department of Obstetrics and Gynecology, Mayo Clinic, Rochester, MN, United States of America.
  • Andrea Mariani
    Department of Advanced Biomedical Sciences, Federico II University, Naples.
  • Bohyun Kim
    Department of Radiology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea. kbh@catholic.ac.kr.
  • Shannon K Laughlin-Tommaso
    Department of Obstetrics and Gynecology, Mayo Clinic, Rochester, MN, United States of America.
  • Timothy L Kline
    Department of Radiology, Mayo Clinic, Rochester, MN, United States of America; Division of Nephrology and Hypertension, Mayo Clinic, Rochester, MN, United States of America. Electronic address: Kline.Timothy@mayo.edu.