Obstetrics & Gynecology

Artificial intelligence-assisted histopathological diagnosis of endocervical gastric-type adenocarcinoma: a multicenter model development and validation study.

Endocervical gastric-type adenocarcinoma (GAS) is one of the most aggressive subtypes of cervical cancer and is frequently underdiagnosed due to morphological ambiguity, leading to delayed diagnosis. Despite the availability of molecular and genomic assays, their high cost, complexity, and limited reproducibility restrict clinical use. This study therefore proposes a highly sensitive artificial in...

Microscopy image segmentation using a fine-tuned machine learning model with limited training dataset.

Manual annotation of microscopic images of thin tissue sections remains time-consuming step limiting the throughput of diagnostic analysis. We present a novel automated approach using the total intensity image as an input to a U-Net architecture of Convolutional Neural Network with a pretrained ResNet-34 encoder for image segmentation. The network was trained on limited-size annotated dataset to d...

Artificial intelligence for translational personalized neoantigen cancer vaccine development.

Personalized neoantigen cancer vaccine is a promising strategy for precision immunotherapy by targeting patient-specific and mutation-derived tumor an...

Vaginal drug delivery for precision therapy in polyendocrine metabolic ovarian syndrome: emerging technologies, translational challenges and future perspectives.

INTRODUCTION: Polyendocrine Metabolic Ovarian Syndrome (PMOS) is an endocrine disorder characterized by metabolic dysfunction, hormonal imbalance, inf...

The design and optimization of a chitosan-based lamotrigine-loaded intranasal mucoadhesive nanomicelle solution using response surface methodology and artificial neural networks.

The study aimed to develop and optimize chitosan-based mucoadhesive nanomicelles for intranasal delivery of lamotrigine (LTG), to enhance epilepsy tre...

Emergent collective dynamics of microrobotic swarms in viscoelastic media: a computational perspective.

Microrobotic swarms are promising candidates for targeted drug delivery in complex physiological environments, including blood, mucus, and extracellul...

Leveraging artificial intelligence in the early detection of ovarian cancer: Development and validation of a risk prediction model.

OBJECTIVE: To develop and validate a predictive tool to estimate the risk of ovarian cancer in a screening setting. METHODS: Machine learning was leve...

Development and validation of a serum peptidomic signature for early detection of asymptomatic ovarian cancer: A multi-center prospective study.

Early detection of asymptomatic ovarian cancer (asym-OC) remains a critical challenge, the failure of which underlies its high mortality. Performing s...

Eliminating Registration Bias in Synthetic CT Generation using a physics-based simulation framework for pelvic anatomy.

Supervised synthetic computed tomography (sCT) generation from cone-beam CT (CBCT) requires spatially registered training pairs, yet perfect registrat...

Non-inferiority analysis of a fetal heart rate artificial intelligence algorithm to registered nurse assessment.

INTRODUCTION: Cardiotocography (CTG) is widely used for monitoring fetal heart rate (FHR) and uterine activity (UA) during pregnancy and labor. Clinic...

Nanomaterial-based drug delivery systems: From intelligent delivery to clinical translation and precision nanomedicine.

Nanomaterial-based drug delivery systems (NDDS) have become enabling technologies for small-molecule reformulation, RNA medicines, vaccines, gene edit...

A Multiparametric Ultrasound Diagnostic Model for Adenomyosis by Integrating Cervical Ultra-Microangiography and Shear Wave Elastography: A Prospective Study.

OBJECTIVE: To develop and validate a multiparametric diagnostic model for adenomyosis utilizing standardized cervical anatomical landmarks. By integra...

Predictive Models of Pregnancy Based on Male Partner Data From a Preconception Cohort Study.

BACKGROUND: Male factors contribute to approximately 50% of couple infertility, yet few studies have used machine learning with comprehensive male par...

Ovarian borderline tumors and minimally invasive surgery: a narrative review.

Borderline ovarian tumors (BOTs) are a distinct subgroup of epithelial ovarian neoplasms that commonly affect women of reproductive age and are associ...

iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data.

Precise resolution of cellular heterogeneity within complex tissues is fundamental to deciphering disease etiologies from bulk transcriptomic profiles...

Label-free ovarian cancer histopathological diagnosis using two-photon autofluorescence microscopy with a joint denoising and segmentation framework.

Two-photon autofluorescence (TPAF) microscopy is a promising modality for rapid, label-free assessment of unstained tissue, but is fundamentally limit...

AI-based segmentation of occult cervical lymph node metastases: potential reduction of cytokeratin staining.

BACKGROUND: Detection of occult cervical lymph node metastases is critical for accurate staging and treatment planning in oral cavity squamous cell ca...

MRI radiomics combined with clinical features for predicting initial dose in focused ultrasound ablation surgery for uterine fibroids.

OBJECTIVE: To develop and validate a radiomics-clinical model for individualized prediction of the initial treatment dose in focused ultrasound ablati...

A Two-Stage Hybrid Feature Selection and Ensemble Learning Framework With Explainable AI for Accurate PCOS Prediction.

BACKGROUND AND AIMS: Polycystic ovary syndrome (PCOS) is the most prevalent endocrine ailment impacting women of reproductive age, distinguished by on...

The Phendo Project: How FemTech Is Advancing Endometriosis Research and Women's Health.

FemTech continues to evolve and advance despite persistent research and funding gaps. In this News and Perspectives article, JMIR Correspondent Jenny ...