Fibroids

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 syste...

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 flo...

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 ques...

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 readi...

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 s...

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 h...

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 surgica...

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 combin...

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 characteri...

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 fe...

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...

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

Myometrial lesions are a common indication for pelvic imaging, with accurate characterisation crucia...

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 significa...

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 a...

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 seriou...

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 ...

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, b...

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) algorith...

Leveraging artificial intelligence for evidence-based recommendations in uterine fibroid therapy: Addressing the unmet need in German healthcare-A clinical trial.

OBJECTIVE: To evaluate the potential of an artificial intelligence (AI)-driven large language model,...

A superpixel based self-attention network for uterine fibroid segmentation in high intensity focused ultrasound guidance images.

Ultrasound guidance images are widely used for high intensity focused ultrasound (HIFU) therapy; how...