Archives of gynecology and obstetrics
Jan 20, 2026
INTRODUCTION: We aimed to develop a machine learning model for first-trimester prediction of gestational diabetes mellitus (GDM) in twin pregnancies using a prospective international, multi-center cohort and identify useful predictive markers. METHOD...
OBJECTIVE: This study aims to both develop and evaluate a predictive model for ureteral access sheath(UAS)placement success using preoperative CT-based 3D ureteral imaging and machine learning techniques. Specifically, it investigates the impact of u...
AIMS: HER2/neu gene is amplified in 15%-20% of invasive breast cancers (IBCs), serving as critical prognostic and predictive marker. HER2-targeted therapies have improved outcomes for HER2-positive patients, highlighting the importance of accurate as...
Journal of neurointerventional surgery
Jan 13, 2026
BACKGROUND: Current clinical decision tools for assessing the risk of symptomatic intracranial hemorrhage (sICH) in patients with vertebrobasilar artery occlusion (VBAO) who received endovascular treatment (EVT) have limited performance. This study d...
BACKGROUND: Machine learning (ML) has been investigated for its predictive value in knee osteoarthritis (KOA) progression. However, systematic evidence on the effectiveness of ML is still lacking, posing a challenge to precision prevention.
Journal of gastrointestinal and liver diseases : JGLD
Dec 26, 2025
BACKGROUND AND AIMS: Differential diagnosis between irritable bowel syndrome (IBS) and inflammatory bowel disease (IBD) represents a major challenge in modern gastroenterology due to overlapping symptoms, limitations of traditional diagnostic methods...
BACKGROUND: Although chest X-rays (CXRs) are widely used, diagnosing mitral stenosis (MS) based solely on CXR findings remains challenging in some cases.
PURPOSE: To develop a machine learning model for predicting stone-free (SF) outcomes following extracorporeal shock wave lithotripsy (SWL) and to identify key clinical and stone-related predictors using interpretable machine learning techniques.
BACKGROUND: Acute respiratory distress syndrome (ARDS) is a common complication after type A aortic dissection surgery and often leads to worsened clinical outcomes for patients. The early prediction of postoperative ARDS is a crucial challenge in cl...
BACKGROUND: Opioid overuse is a costly and significant problem in the United States. Medical specialties including surgery are a contributor to opioid prescriptions while having few clear prescribing guidelines. Machine learning predictive tools can ...
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