Fibroids

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, ChatGPT 4.0, to provide personalized, evidence-based treatment recommendations for uterine fibroids. METHODS: ChatGPT 4.0 was trained using evidence-based data from Uptodate and German medical literature. The algorithm generated individualized recommendations based on clinical characteristics and p...

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; however, the speckles, acoustic shadows, and signal attenuation in ultrasound guidance images hinder the observation of the images by radiologists and make segmentation of ultrasound guidance images more difficult. To address these issues, we proposed the superpixel based attention network, a network i...

Robot-Assisted Laparoscopic Hysterectomy with Artificial Intelligence-Analyzed Imaging: Robot-Assisted Laparoscopic Hysterectomy with AI Imaging.

A Novel and Modern Calculator to Predict Vaginal Birth after Cesarean Delivery.

Counseling patients who are considering a trial of labor after cesarean (TOLAC) is a challenging task given the risks and benefits of either approach....

Laparoscopic two-port myomectomy-A retrospective case series of a novel minimally invasive approach.

OBJECTIVE: This study presents a novel two-port technique for laparoscopic myomectomy, examining perioperative outcomes from 87 cases DESIGN: Retrospe...

Cooperative Magnetic Interfacial Microrobot Couple for Versatile Non-Contact Biomedical Applications.

Magnetic interfacial microrobots are increasingly recognized as a promising approach for potential biomedical applications ranging from electronic fun...

Predicting Robotic Hysterectomy Incision Time: Optimizing Surgical Scheduling with Machine Learning.

BACKGROUND AND OBJECTIVES: Operating rooms (ORs) are critical for hospital revenue and cost management, with utilization efficiency directly affecting...

Deep learning based uterine fibroid detection in ultrasound images.

Uterine fibroids are common benign tumors originating from the uterus's smooth muscle layer, often leading to symptoms such as pelvic pain, and reprod...

Development and validation of a deep learning-based method for automatic measurement of uterus, fibroid, and ablated volume in MRI after MR-HIFU treatment of uterine fibroids.

INTRODUCTION: The non-perfused volume divided by total fibroid load (NPV/TFL) is a predictive outcome parameter for MRI-guided high-intensity focused ...

Deep Learning for Grading Endometrial Cancer.

Endometrial cancer is the fourth most common cancer in women in the United States, with a lifetime risk of approximately 2.8%. Precise histologic eval...

Empowering gynaecologists with Artificial Intelligence: Tailoring surgical solutions for fibroids.

BACKGROUND: In recent years, the integration ofArtificial intelligence (AI) into various fields of medicine including Gynaecology, has shown promising...

Distinguishing the Uterine Artery, the Ureter, and Nerves in Laparoscopic Surgical Images Using Ensembles of Binary Semantic Segmentation Networks.

Performing a minimally invasive surgery comes with a significant advantage regarding rehabilitating the patient after the operation. But it also cause...

Machine learning models for prediction of postoperative venous thromboembolism in gynecological malignant tumor patients.

AIM: To identify risk factors that associated with the occurrence of venous thromboembolism (VTE) within 30 days after hysterectomy among gynecologica...

A lightweight hybrid model for the automatic recognition of uterine fibroid ultrasound images based on deep learning.

PURPOSE: Uterine fibroids (UF) are the most frequent tumors in ladies and can pose an enormous threat to complications, such as miscarriage. The accur...

Use of natural language processing to uncover racial bias in obstetrical documentation.

Natural Language Processing (NLP), a form of Artificial Intelligence, allows free-text based clinical documentation to be integrated in ways that faci...

Introducing surgical intelligence in gynecology: Automated identification of key steps in hysterectomy.

OBJECTIVE: The analysis of surgical videos using artificial intelligence holds great promise for the future of surgery by facilitating the development...

Gasless transvaginal natural orifice transluminal endoscopic surgery for hysterectomy and salpingectomy on a robot platform with flexible devices in a porcine model.

In this report, we described a new technique of gasless V-NOTES for hysterectomy and salpingectomy on a robotic platform with flexible devices in a po...

Validation of a Simulation Model for Robotic Myomectomy.

STUDY OBJECTIVE: Several simulation models have been evaluated for gynecologic procedures such as hysterectomy, but there are limited published data f...

Validate robot-assisted total laparoscopic hysterectomy with four equally-spaced ports without an assistant port.

To evaluate the usefulness of robot-assisted total laparoscopic hysterectomy with four equally-spaced ports (RA-TLH/4e) without an assistant port. In ...

Gynecologic Surgical Subspecialty Training Decreases Surgical Complications in Benign Minimally Invasive Hysterectomy.

STUDY OBJECTIVE: To evaluate the impact of gynecologic subspecialty training on surgical outcomes in benign minimally invasive hysterectomies (MIHs) w...