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

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).

Publication DOI: https://doi.org/10.3233/SHTI230073
Divisions: College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies
College of Engineering & Physical Sciences > School of Computer Science and Digital Technologies > Software Engineering & Cybersecurity
Additional Information: © 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).
Event Title: 33rd Medical Informatics Europe Conference: Caring is Sharing - Exploiting the Value in Data for Health and Innovation, MIE2023
Event Type: Other
Event Dates: 2023-05-22 - 2023-05-25
Uncontrolled Keywords: Artificial Intelligence,Diagnosis time,Endometriosis,Female reproductive health,Menstrual health,Predictions models,Biomedical Engineering,Health Informatics,Health Information Management
ISBN: 978-1-64368-388-1, 978-1-64368-389-8
Last Modified: 03 May 2024 07:26
Date Deposited: 08 Nov 2023 16:02
Full Text Link:
Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
https://ebooks. ... 3233/SHTI230073 (Publisher URL)
PURE Output Type: Conference contribution
Published Date: 2023-05-18
Authors: Hine, Ariane
Bowles, Juliana
Webber, Thais (ORCID Profile 0000-0002-8091-6021)

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