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Blix, E., Maude, R., Hals, E., Kisa, S., Karlsen, E., Nohr, E.A., et al. (2019) Intermittent Auscultation Fetal Monitoring during Labour: A Systematic Scoping Review to Identify Methods, Effects, and Accuracy. PLoS ONE, 14, e0219573.
https://doi.org/10.1371/journal.pone.0219573

has been cited by the following article:

• TITLE: Utilization of Artificial Intelligence for Diagnosis and Management of Urinary Incontinence in Women Residing in Areas with Low Resources: An Overview

  AUTHORS: Amad Qureshi, Aanchal Mathur, Jonia Alshiek, S. Abbas Shobeiri, Qi Wei

  KEYWORDS: Urinary Incontinence, Artificial Intelligence, Women’s Health, Underdeveloped Regions

  JOURNAL NAME: Open Journal of Obstetrics and Gynecology, Vol.11 No.4, April 25, 2021

  ABSTRACT: Urinary incontinence (UI) is a distressing condition involving involuntary loss of urine from the body. Urinary incontinence can negatively impact a person’s overall quality of life and lead them into stages of embarrassment and depression. It is an underrepresented and undertreated condition prevalent in women, especially in low socioeconomic regions where women may not be able to express their concerns due to unawareness of diagnosis and treatment/management options. There are different diagnostic and management protocols for UI; however, utilizing artificially intelligent systems is not standard care. This paper overviews the use of artificial intelligence in women’s health and as a means of cost-effectively diagnosing patients, and as an avenue for providing low-cost treatments to women that suffer from urinary incontinence in low-resource communities. Studies found that these systems, mainly utilizing artificial neural networks (ANNs) and convolutional neural networks (CNNs), served to be an effective method in diagnosing patients and providing an avenue for personalized treatment for improved patient outcomes. A simple artificial intelligence (AI) model utilizing Multilayer Perceptron (MLP) Networks was proposed to diagnose and manage urinary incontinence.