Artificial Intelligence in the Diagnosis and Imaging-Based Assessment of Pelvic Organ Prolapse: A Scoping Review

• Published in: Medicina (Kaunas, Lithuania) Vol. 61; no. 8; p. 1497
• Main Authors: Botoncea, Marian, Molnar, Călin, Butiurca, Vlad Olimpiu, Nicolescu, Cosmin Lucian, Molnar-Varlam, Claudiu
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 21.08.2025; MDPI
• Subjects: Artificial intelligence; Artificial Intelligence - standards; Artificial Intelligence - trends; Classification; convolutional neural networks; Data analysis; Deep Learning; Diagnostic imaging; Evidence-based medicine; Female; Humans; Machine learning; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Medical imaging; Medical imaging equipment; Medical Subject Headings-MeSH; Neural networks; Pelvic organ prolapse; Pelvic Organ Prolapse - diagnosis; Pelvic Organ Prolapse - diagnostic imaging; Pelvis; Prolapse; Review; Ultrasonic imaging; Ultrasonography - methods; Vagina; vision transformers; anatomical segmentation; deep learning; magnetic resonance imaging; vision transformers; convolutional neural networks; pelvic organ prolapse; artificial intelligence; medical imaging; ultrasound
• ISSN: 1648-9144; 1010-660X; 1648-9144
• DOI: 10.3390/medicina61081497

Background and Objectives: Pelvic organ prolapse (POP) is a complex condition affecting the pelvic floor, often requiring imaging for accurate diagnosis and treatment planning. Artificial intelligence (AI), particularly deep learning (DL), is emerging as a powerful tool in medical imaging. This scoping review aims to synthesize current evidence on the use of AI in the imaging-based diagnosis and anatomical evaluation of POP. Materials and Methods: Following the PRISMA-ScR guidelines, a comprehensive search was conducted in PubMed, Scopus, and Web of Science for studies published between January 2020 and April 2025. Studies were included if they applied AI methodologies, such as convolutional neural networks (CNNs), vision transformers (ViTs), or hybrid models, to diagnostic imaging modalities such as ultrasound and magnetic resonance imaging (MRI) to women with POP. Results: Eight studies met the inclusion criteria. In these studies, AI technologies were applied to 2D/3D ultrasound and static or stress MRI for segmentation, anatomical landmark localization, and prolapse classification. CNNs were the most commonly used models, often combined with transfer learning. Some studies used hybrid models of ViTs, demonstrating high diagnostic accuracy. However, all studies relied on internal datasets, with limited model interpretability and no external validation. Moreover, clinical deployment and outcome assessments remain underexplored. Conclusions: AI shows promise in enhancing POP diagnosis through improved image analysis, but current applications are largely exploratory. Future work should prioritize external validation, standardization, explainable AI, and real-world implementation to bridge the gap between experimental models and clinical utility.