Fibroids

Liquid Biopsy Cell-free RNA-based Machine Learning Enables Preoperative Risk-Stratification of Uterine Leiomyosarcoma.

BACKGROUND: Accurate preoperative distinction between uterine leiomyoma (UM) and uterine leiomyosarcoma (UMS) remains a major clinical challenge. Misclassification can lead to inadvertent dissemination of occult malignancy during minimally invasive procedures, while cautious management increases the use of more invasive surgery with greater morbidity. Current diagnostic approaches, including imagi...

Deep learning for intraoperative recognition of critical structures in total hysterectomy.

BACKGROUND: This study aimed to develop and evaluate a deep learning-based surgical navigation system capable of recognizing the ureter, uterine artery, and bladder-uterine dissection plane during minimally invasive gynecologic surgery. METHODS: An artificial intelligence (AI) model was developed at the University of Tokyo Hospital using videos of prior surgeries. Surgical videos of 27 laparoscopi...

Integration of two-dimensional microvascular flow imaging and ultrasound scoring for prediction of placenta accreta spectrum: A prospective study.

OBJECTIVE: This study evaluates the diagnostic performance of two-dimensional (2D) microvascular flow imaging combined with standardized ultrasound ma...

A Deep Learning Perspective on Preoperative Ultrasound and Intraoperative Discordance in Transcervical Fibroid Ablation.

Quality of AI-generated post-operative patient education after minimally invasive gynecologic surgery: A comparative analysis.

STUDY OBJECTIVE: To compare the quality of AI-generated responses to gynecologic post-operative questions with educational materials published by prof...

Artificial Intelligence in Minimally Invasive Gynecological Surgery: A Systematic Review of Task- Specific Performance and Clinical Translational Readiness.

OBJECTIVE: To systematically evaluate the task-specific performance and clinical translational readiness of artificial intelligence (AI) applications ...

Fetal Fraction and Uterine Leiomyoma Volume: New Insights From Interpretable Modeling.

OBJECTIVE: This study aims to develop a predictive model to estimate the likelihood of achieving a sufficient fetal fraction (FF) for non-invasive pre...

Whole-transcriptome sequencing and machine learning detect molecular signatures of endometrial cancer in non-invasive vaginal swabs.

OBJECTIVE: Current tissue-based methods for ruling out endometrial cancer in symptomatic women are highly invasive. We explored whether non-invasive v...

Deep learning for Evaluation and Prediction of TecHnical Skills in robotic-assisted vaginal cuff closure (DEPTHS) study.

BACKGROUND: To support surgical education, there has been an increasing focus on integrating surgical data, including surgical motion, activity and pr...

Multimodal MRI radiomics for predicting HIFU ablation efficacy in uterine fibroids: a machine learning study.

OBJECTIVE: To explore the predictive value of machine learning-based multimodal MRI radiomics combined with clinical features in the efficacy of high-...

Prediction of surgery type for uterine fibroids using machine learning algorithms and hormone values.

This study aimed to develop and externally validate machine learning (ML)-based models to characterize surgical classification patterns between hyster...

Radiomics-based ultrasOund Model for differentiating Uterine Sarcomas from leiomyomas (ROMUS): a retrospective pilot Multicenter Italian Trials in Ovarian Cancer (MITO) study.

OBJECTIVE: To develop machine-learning models that incorporate clinical information and radiomics features extracted from ultrasound images to disting...

Endometriosis pain index: development of a model to predict poor pain-related quality of life after endometriosis surgery through machine learning analysis of registry data.

Predictive tools are lacking for pain-related outcomes after endometriosis surgery. The objective of this study was to develop and validate a machine ...

Magnetic resonance imaging (MRI) of the myometrium - benign and malignant disease.

Myometrial lesions are a common indication for pelvic imaging, with accurate characterisation crucial for guiding management. While ultrasonography re...

Machine learning models for identifying urinary incontinence in women with a history of hysterectomy using basic demographic and clinical characteristics: A cross-sectional study.

BACKGROUND: Urinary incontinence (UI) in women with a history of hysterectomy represents a significant global health concern. It is crucial to clarify...

Machine Learning Prediction of Incomplete Hysteroscopic Myomectomy Using Preoperative Clinical and Imaging Variables.

STUDY OBJECTIVE: To develop and validate a machine-learning (ML) model using preoperative clinical and imaging variables including ultrasound and diag...

Semantic segmentation deep learning model boosts surgeons' organ recognition in minimally invasive hysterectomy - a prospective multi-center reader performance study using pre-selected video clips.

BACKGROUND: Injury to the ureter and bladder during minimally invasive hysterectomy remains a serious complication, often resulting from insufficient ...

Subtype classification of gastric spindle cell tumors in whole slide images.

AIMS: Accurate cancer subtype classification is critical due to variations in tumor progression and prognosis. Traditionally, pathologists classified ...

An interpretable model based on weakly supervised learning for uterine smooth muscle tumor diagnosis: A multi-center study.

Uterine smooth muscle tumors (USMTs) are the most common tumors of the female reproductive system, but remain diagnostically challenging due to morpho...

Machine learning-based prediction of postpartum hemorrhage using maternal clinical and biochemical features.

OBJECTIVE: This study evaluates the predictive performance of various machine learning (ML) algorithms for postpartum hemorrhage (PPH), peripartum hys...