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Pelvic Organ Prolapse: Clinical Updates and Perspectives
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Pelvic Organ Prolapse: Clinical Updates and Perspectives

A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Obstetrics & Gynecology".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1362

Special Issue Editor

Dr. José Antonio García-Mejido

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Guest Editor
Department of Surgery, University of Seville, 41004 Seville, Spain
Interests: pelvic floor; ultrasound; prolapse organ pelvic; levator ani muscle; urogynecology; uterine prolapse

Special Issue Information

Dear Colleagues,

Pelvic organ prolapse (POP) is an increasingly common disease that affects women and substantially interferes with the quality of life of those who suffer from it. Approximately 30% of corrective surgeries for POP are due to recurrences. Therefore, it is important to correctly diagnose the pathology, as well as to understand the causal mechanisms of POP in order to reduce the recurrence of POP.

Classically, the diagnosis of POP has been through clinical examination based on the POP-Q system. However, the pelvic organ prolapse quantification (POP-Q) system is a methodology that presents limitations, especially in patients who are going to undergo corrective surgery for POP. Recently, the application of different imaging techniques and the development of new diagnostic models have allowed us to diagnose POP in a more objective manner, presenting less intra- and inter-observer variability, especially in those POPs with surgical indication. In the future, these diagnostic advances will change the diagnostic methodology of POP. Therefore, professionals dedicated to diagnosing and treating POP must be aware of the diagnostic advances in this field in order to improve the treatment of our patients.

Dr. José Antonio García-Mejido
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Clinical Medicine is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pelvic floor
  • ultrasound
  • pelvic organ prolapse
  • urogynecology
  • uterine prolapse
  • cystocele
  • rectocele
  • pelvic organ prolapse quantification system

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Published Papers (1 paper)

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Research

14 pages, 1972 KiB  
Article
Ultrasound Diagnosis of Pelvic Organ Prolapse Using Artificial Intelligence
by José Antonio García-Mejido, Juan Galán-Paez, David Solis-Martín, Fernando Fernández-Palacín, Ana Fernández-Palacín and José Antonio Sainz-Bueno
J. Clin. Med. 2025, 14(11), 3634; https://doi.org/10.3390/jcm14113634 - 22 May 2025
Viewed by 1089
Abstract
Background/Objectives: The aim of this study was to design a fully automated hybrid AI-based method, combining a convolutional neural network (CNN) and a tree-based model (XGBoost), which was capable of diagnosing different pelvic organ prolapses (POPs) in a dynamic two-dimensional ultrasound study from [...] Read more.
Background/Objectives: The aim of this study was to design a fully automated hybrid AI-based method, combining a convolutional neural network (CNN) and a tree-based model (XGBoost), which was capable of diagnosing different pelvic organ prolapses (POPs) in a dynamic two-dimensional ultrasound study from the midsagittal plane. Methods: This was a prospective observational study with 188 patients (99 with POP and 89 without POP). Transperineal pelvic floor ultrasound videos were performed, and normality or POP was defined. These videos were subsequently labeled, and an algorithm was designed to detect POP based on three phases: 1. Segmentation—a CNN was used to locate and identify the visible pelvic organs in each frame of the ultrasound video. The output had a very high dimensionality. 2. Feature engineering and dataset construction—new features related to the position and shape of the organs detected using the CNN were generated. 3. The POP predictive model—this was created from the dataset generated in the feature engineering phase. To evaluate diagnostic performance, accuracy, precision, recall, and F1-score were considered, along with the degree of agreement with the expert examiner. Results: The best agreements were observed in the diagnosis of cystocele and uterine prolapse (88.1%) and enterocoele (81.4%). The proposed methodology showed an accuracy of 96.43%, an overall accuracy of 98.31%, a recall of 100%, and an F1-score of 98.18% in detecting the presence of POP. However, when differentiating between the various types of POP, we observed that the precision, accuracy, recall, and F1-score were higher when detecting cystocele and uterine prolapse. Conclusions: We have developed the first predictive model capable of diagnosing POP in a dynamic, bi-dimensional ultrasound study from the midsagittal plane using deep learning and machine learning techniques. Full article
(This article belongs to the Special Issue Pelvic Organ Prolapse: Clinical Updates and Perspectives)
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