Latest AI and machine learning research in obstetrics & gynecology for healthcare professionals.
OBJECTIVE: Endometriosis is a prevalent gynecological disease characterized by the ectopic growth of functional endometrial tissue outside the uterine cavity, affecting millions of women worldwide. Currently, the definitive diagnosis relies on invasive laparoscopy (the gold standard), with an average diagnostic delay of 7-10 years from symptom onset. Non-invasive biomarkers from blood or endometri...
Ovarian cancer represents one of the most lethal gynecologic malignancies, characterized by low early detection rates and challenging prognostic assessment. Conventional diagnostic modalities demonstrate limited sensitivity and specificity for early-stage disease identification. Recent research has begun to explore causal inference methodologies as complementary approaches that may enhance diagnos...
INTRODUCTION: Endometrial cancer is one of the most common gynecologic malignancies, with increasing incidence, in developed countries. Advances in mi...
Safe deployment of auto-contouring models requires the inclusion of automated QA. One such approach is to use two independent auto-contouring models a...
Hypoxia arises in most solid tumors with insufficient blood flow, which hinders the delivery and efficacy of therapeutic agents to tumors. In this wor...
Engineered lipid nanoparticles (LNPs) represent a breakthrough in targeted drug delivery, enabling precise spatiotemporal control essential to treat c...
Reproductive aging impacts women's health through fertility decline, disease susceptibility, and systemic aging. This study explores the retinal age g...
The transformative advancements in artificial intelligence (AI) have significantly impacted medical fields, particularly obstetrics and gynecology (OB...
Nanobodies offer significant therapeutic potential due to their small size, stability, and versatility. Although advancements in computational protein...
Automated insulin delivery (AID) systems have revolutionized diabetes care by integrating continuous glucose monitoring (CGM), insulin pumps, and adva...
AIM: We aimed to develop a machine-learning(ML) algorithm consisting of physical examination, sonographic findings, and laboratory markers.
OBJECTIVE: The rise of artificial intelligence (AI) and large language models like Llama, Gemini, or Generative Pretraining Transformer (GPT) signals ...
BACKGROUND: Endometriosis and breast cancer are significant global health burdens affecting women worldwide. Both conditions share notable characteris...
Circulating tumor cells (CTCs) are promising biomarkers for cancer diagnosis, while detecting CTCs in clinical samples is still challenging due to the...
PURPOSE: Uterine sarcoma is a rare disease whose association with body composition parameters is poorly understood. This study explored the impact of ...
BACKGROUND: Automatic detection of surgical instruments is essential for Artificial Intelligence Surgery. This study aimed to construct a large-scale ...
BACKGROUND: Polycystic ovary syndrome (PCOS) is a common endocrinopathy among women that requires self-management to improve mental and physical healt...
The development of therapeutics builds on testing their efficiency in vitro. To optimize gene therapies, for example, fluorescent reporters expressed ...
Subcutaneous (SC) administration of monoclonal antibodies (mAbs) offers patient-centric benefits such as self-administration, fewer hospital visits, a...
BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition with increasing prevalence worldwide. Air pollution may be a major contri...