The need for a non-invasive technology for endometriosis detection and care

Ariane Alice Hine*, Juliana Kuster Filipe Bowles, Thais Webber

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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 - exploting the value in data for health and innovation
Subtitle of host publicationproceedings of MIE 2023
EditorsMaria Hägglund, Madeleine Blusi, Stefano Bonacina, Lina Nilsson, Inge Cort Madsen, Sylvia Pelayo, Anne Moen, Arriel Benis, Lars Lindsköld, Parisis Gallos
Place of PublicationAmsterdam
PublisherIOS Press
Pages103 - 107
Number of pages5
ISBN (Electronic)9781643683898
ISBN (Print)9781643683881
DOIs
Publication statusPublished - 18 May 2023
EventMedical Informatics Europe 2023 (MIE 2023) - Göteborg, Sweden
Duration: 22 May 202325 May 2023
https://www.mie2023.org/home-page

Publication series

NameStudies in health technology and informatics
Volume302
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

ConferenceMedical Informatics Europe 2023 (MIE 2023)
Abbreviated titleMIE 2023
Country/TerritorySweden
CityGöteborg
Period22/05/2325/05/23
Internet address

Keywords

  • Female reproductive health
  • Endometriosis
  • Artifical intelligence
  • Prediction models
  • Diagnosis time
  • Menstrual health

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