The Need for a Non-Invasive Technology for Endometriosis Detection and Care

Ariane Hine*, Juliana Bowles, Thais Webber

*Corresponding author for this work

Research output: Chapter in Book/Published conference outputConference publication

Abstract

Endometriosis is a complex, poorly understood, female health condition that can markedly reduce a woman's quality of life. The gold-standard diagnostic method for Endometriosis is invasive laparoscopic surgery, which is costly, not timely, and comes with risks to the patient. We argue that the need for a non-invasive diagnosis procedure, higher quality of patient care and reduced diagnosis delay, can be fulfilled by advances and research to devise innovative computational solutions. To leverage computational and algorithmic techniques, enhanced data recording and sharing are vital. We discuss the potential benefits of using personalised computational healthcare on both the clinician and patient side, reducing the lengthy average diagnosis time (currently around 8 years).

Original languageEnglish
Title of host publicationCaring is Sharing - Exploiting the Value in Data for Health and Innovation - Proceedings of MIE 2023
EditorsMaria Hagglund, Madeleine Blusi, Stefano Bonacina, Lina Nilsson, Inge Cort Madsen, Sylvia Pelayo, Anne Moen, Arriel Benis, Lars Lindskold, Parisis Gallos
Pages103-107
Number of pages5
Volume302
ISBN (Electronic)978-1-64368-389-8
DOIs
Publication statusPublished - 18 May 2023
Event33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023 - Gothenburg, Sweden
Duration: 22 May 202325 May 2023

Publication series

NameStudies in Health Technology and Informatics
Volume302
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023
Country/TerritorySweden
CityGothenburg
Period22/05/2325/05/23

Bibliographical note

© 2023 European Federation for Medical Informatics (EFMI) and IOS Press.
This article is published online with Open Access by IOS Press and distributed under the terms
of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).

Keywords

  • Artificial Intelligence
  • Diagnosis time
  • Endometriosis
  • Female reproductive health
  • Menstrual health
  • Predictions models

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