Obstetrics & Gynecology

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 enhancing patients' quality of life. As the third most prevalent gynecologic cancer globally, ovarian cancer remains challenging to diagnose due to the high cost, limited accessibility, and radiation exposure associated with current screening techniques. This study integrates surface-enhanced Raman s...

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

INTRODUCTION: Polycystic ovary syndrome (PCOS) is a lifelong endocrine-metabolic condition with prominent dermatologic manifestations such as hirsutism, acne/seborrhea, and female pattern hair loss (FPHL), which are frequently the initial complaint for seeking medical attention. AREAS COVERED: This review explores the pathogenesis and dermatologic presentation of PCOS across the lifespan, emphasiz...

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 environmental factors such as exposure to per- and polyf...

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 biologically compatible vehicles for targeted drug deli...

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 characterized by the accumulation of pathological prot...

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 worldwide. Despite extensive research, its cellular...

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 observational data. This challenge is particularly...

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 sagittal measurements, explicitly addressing C7 ob...

The role of artificial intelligence in pre-operative prediction of completeness of cytoreduction for peritoneal surface malignancies: a scoping review.

BACKGROUND: Complete cytoreduction is the most important prognostic factor for patients with peritoneal surface malignancies (PSM) and its prediction ...

Automated gross tumour volume delineation on T2-weighted magnetic resonance imaging coupled with radiomics for predicting cervical cancer recurrence.

PURPOSE/OBJECTIVE(S): Cervical cancer patients are at a high risk of tumour recurrence. Accurate delineation of the primary gross tumour volume (GTV) ...

Novel Lipid Nanoparticle (LNP) Delivery Systems Enabling the Advancement of RNA Therapeutics.

Lipid nanoparticle (LNP)-mRNA technology has profoundly transformed the biomedical landscape, establishing itself as a highly versatile and powerful p...

The utility of artificial intelligence and deep learning to automate and accelerate follicle counts in human ovarian tissue†.

Follicles comprised of oocytes and surrounding cells are essential for reproductive function. They are fixed before birth and decrease thereafter thro...

Clinical-grade autonomous cytopathology through whole-slide edge tomography.

Cytopathology, often abbreviated as cytology, has a central role in the early detection of cancer, such as cervical, lung and bladder cancers, owing t...

A Novel Approach to Ovarian Cancer Diagnosis via CT Imaging: GPT-4o-Driven Automated Feature Recognition and Validation in Clinical Settings.

BACKGROUND: Accurate non-invasive diagnosis of early-stage ovarian cancer remains challenging because of the limited number of biomarkers. Although ar...

Germline Pathogenic Variant Prediction Model for Tumor-Only Sequencing Based on Japanese Clinicogenomic Database.

PURPOSE: Germline pathogenic variants (GPV) are frequently identified as secondary findings in cancer gene panel testing. Due to limited data on germl...

3D deep-learning radiomics from MR-T2WI for predicting placenta accreta spectrum disorders: A multicenter study.

OBJECTIVE: To develop a three-dimensional (3D) deep-learning radiomics from magnetic resonance-T2-weighted imaging (T2WI) for predicting the risk of p...

Title: AI-based quantification of tumor-infiltrating lymphocytes with integrative transcriptomics in ovarian clear cell carcinoma: JGOG3025-TR1/A1 study.

Transcriptomic classification methods have been proposed for ovarian clear cell carcinoma. However, their clinical significance and association with p...

Machine learning-predicted insulin resistance is a risk factor for 12 types of cancer.

Insulin resistance is suggested to be a risk factor for cancer; however, large-scale epidemiological evidence linking insulin resistance to cancer rem...

Estimating the odds ratio from the output scores of machine learning models: possibilities and limitations.

Estimation of exposure-response association is central to epidemiologic research. Although the advantages of machine learning (ML) techniques for mode...

Evaluation of AI language models in answering pregnancy-related questions assessed by obstetrics specialists.

This study aimed to compare the performance of three large language models-ChatGPT-3.5, Gemini, and ChatGPT-4.0-in generating responses to ten frequen...