Obstetrics & Gynecology

Development and interpretability of an XGBoost model to predict high grade cervical intraepithelial neoplasia.

Ultrasound-Responsive Serotonylation and Hallmark Pathway Gene Signatures Reveal Tumor Microenvironment Vulnerabilities and Prognostic Subtypes in Colorectal Cancer.

BACKGROUND: Focused ultrasound, low-intensity focused ultrasound, and microbubble-enhanced sonoporation are examples of ultrasound-based cancer therapies that have shown promise as biophysical modalities for enhancing drug penetration, immunogenic cell death, and targeted delivery of radiopharmaceuticals in solid tumors. The molecular factors controlling ultrasonic therapy receptivity, however, ar...

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 ...

Targeting metabolic vulnerabilities with advanced delivery systems.

Metabolism modulation has emerged as a promising frontier in precision oncology. Nonetheless, the primary gap is the inability to precisely target the...

Emily vs. Lakisha: Quantifying socioeconomic and structural disparities in AI-generated ovarian cancer care plans.

BACKGROUND: Rapid integration of Large Language Models (LLMs) into oncology workflows mandates scrutiny of implicit biases that may exacerbate health ...

Integrative metabolomics and machine learning reveal the toxic mechanisms of elaidic acid in polycystic ovary syndrome.

BACKGROUND: Polycystic ovary syndrome (PCOS) is characterized by reproductive dysfunction and metabolic disturbances, including obesity, insulin resis...

LUMI-lab: A foundation model-driven autonomous platform enabling discovery of ionizable lipid designs for mRNA delivery.

Integrating AI with robotics offers a promising approach to molecular discovery and optimization, enabling efficient exploration of vast chemical spac...

Distinction between primary and metastatic mucinous ovarian carcinoma from histopathology images using deep learning.

Accurately distinguishing between primary and gastrointestinal metastatic mucinous ovarian carcinoma (MOC) is crucial but remains highly challenging. ...

Agentic AI and the rise of in silico team science in biomedical research.

Agentic artificial intelligence (AI) systems are emerging as teams of intelligent computational experts capable of rivaling human performance in labor...

A dual-center study: multimodal fusion-based deep learning approach for pathological subtype prediction of type I and type II ovarian cancer.

End-to-end integrative segmentation and radiomics prognostic models for risk stratification of high-grade serous ovarian cancer: a retrospective multicohort study.

BACKGROUND: Valid stratification factors for patients with epithelial ovarian cancer are still lacking and individualisation of care remains an unmet ...

Artificial intelligence as a diagnostic support tool in hysteroscopy: current evidence and clinical implications.

BACKGROUND: Hysteroscopy allows direct inspection of the uterine cavity for many conditions. Despite being widely adopted, its diagnostic accuracy lar...

Construction and implementation of an AI-enhanced progressive training model for clinical competency development in obstetrics and gynecology residents.

Classification of knowledge of fertility period among adolescent girls in East Africa from 2012 to 2022: Machine learning algorithm.

Understanding the time of the menstrual cycle would help women to avoid getting pregnant without the need for surgical, hormonal, or mechanical contra...

AI-based imputation of anti-Müllerian hormone enables robust prediction of oocyte retrieval during controlled ovarian stimulation.

PURPOSE: This study addressed the practical challenge of missing data in assisted reproductive technology by evaluating the reliability of predicting ...

Inflammatory indices, machine learning and artificial intelligence in tubal ectopic pregnancy management.

OBJECTIVE: To assess the predictive value of hematologic and biochemical inflammatory indices for methotrexate (MTX) treatment outcomes in tubal ectop...

Identification of a Potential Biomarker PDGFRA for Endometriosis Based on Comprehensive Analysis of Multiple Omics Data.

Endometriosis (EMs) is a common chronic inflammatory gynecological disorder. But the exact pathogenetic mechanism of the disease is not clear, with so...

A deep learning-based computer-aided determination method for dominant follicle identification and antral follicle count in pelvic MRI images.

OBJECT: To develop and evaluate a multi-stage computer-aided determination (CAD) method for automated antral follicle count (AFC) and dominant follicl...

Modelling Follicular Growth During Ovarian Stimulation Using Agent-based Artificial Intelligence.

CONTEXT: Ovarian stimulation is a key step in medically assisted reproduction (MAR), whereby supraphysiological doses of FSH extend the "FSH window" a...

AID-FGS: Artificial intelligence-enabled diagnosis of female genital schistosomiasis: Preliminary findings.

Female genital schistosomiasis (FGS) is a sequela of infection with a waterborne parasite prevalent in sub-Saharan Africa and is associated with incre...