Obstetrics & Gynecology

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

Accurately distinguishing between primary and gastrointestinal metastatic mucinous ovarian carcinoma...

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 expe...

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 lacki...

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...

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...

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 techn...

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 me...

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 pathogene...

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 automate...

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

CONTEXT: Ovarian stimulation is a key step in medically assisted reproduction (MAR), whereby supraph...

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 ...

Non-invasive screening for ovarian cancer by combining serum SERS with interpretable machine learning models.

Early identification of malignant ovarian tumors is critical for informing treatment decisions and e...

Skin deep: dermatologic challenges in PCOS through the female lifespan.

INTRODUCTION: Polycystic ovary syndrome (PCOS) is a lifelong endocrine-metabolic condition with prom...

Multi-level evidence reveals KCTD15 and FAM19A2 as key targets in PFOA/PFOS-mediated PCOS pathogenesis.

BACKGROUND: Polycystic ovary syndrome (PCOS), a common endocrine disorder, has been linked to enviro...

Nanoengineering of Exosomal Surfaces for Precision Targeting and Payload Delivery at the Molecular Level.

Exosomes, nano-sized extracellular vesicles secreted by almost all cell types, have emerged as biolo...

Artificial intelligence-driven nano-enhanced stem cell therapy for neurodegenerative diseases: from rational design to clinical translation.

Neurodegenerative diseases (NDs) are progressive and incurable central nervous system disorders char...

Integration of Raman tweezers and machine learning for label-free single-cell characterization of endometriosis cells.

Endometriosis occurs when endometrial tissue grows outside the uterus, affecting millions of women w...

Healthcare applications of 0-1 neural networks in prescriptive problems with observational data.

A key challenge in medical decision making is learning treatment policies for patients with limited ...

Hierarchical deep learning pipeline for robust cervical parameter measurement in radiographs with C7 obscuration.

We developed and externally validated a hierarchical deep learning pipeline that automates cervical ...