Journal of neurointerventional surgery
Nov 18, 2025
BACKGROUND: We investigate the association of imaging biomarkers extracted from fully automated body composition analysis (BCA) of computed tomography (CT) angiography images of endovascularly treated acute ischemic stroke (AIS) patients regarding an...
BACKGROUND: Three-vessel coronary artery disease (TVD) is a severe subtype of coronary heart disease, strongly associated with inflammation and metabolic dysfunction. The C-reactive protein-triglyceride glucose index (CTI), an integrated measure of i...
BACKGROUND: Accurate prediction of pregnancy outcomes in assisted reproductive technology (ART) remains a clinical challenge due to the complexity and heterogeneity of IVF/ICSI cycles. Existing models often focus on isolated treatment stages and rely...
BACKGROUND: This study sought to identify critical body composition characteristics associated with surgical difficulty in Laparoscopic Total Mesorectal Excision (LaTME) and to develop and validate an interpretable machine learning model using body c...
OBJECTIVE: To develop and validate an interpretable delta ultrasound radiomics model for predicting live birth following single vitrified-warmed blastocyst transfer (SVBT).
PURPOSE: To evaluate the postoperative pneumonia (POP) risk of patients with non-small cell lung cancer (NSCLC), identify influencing factors, develop a LASSO regression-based model to predict POP risk and identify critical influencing factors.
BACKGROUND: Fetal growth restriction (FGR) contributes to over 30% of late-pregnancy stillbirth, yet its diagnosis is challenging because current methods rely on indirect surrogate markers (estimated fetal weight and umbilical artery) that often fail...
This study aimed to develop a novel diagnostic tool for detecting meningioma using skull X-ray images, combining deep learning with traditional machine learning classifiers. The goal was to explore the potential of using a cost-effective and widely a...
International journal of colorectal disease
Nov 17, 2025
PURPOSE: Current diagnostic modalities lack sufficient sensitivity for detecting omental metastasis (OM), often underestimating metastatic burden. Unlike traditional statistical model, machine learning (ML) model is designed to detect subtle variable...
BACKGROUND: HIV/TB co-infection presents substantial public-health challenges, showing greater treatment-failure and mortality rates than tuberculosis alone. Recent advances in machine learning (ML) provide a robust means of identifying high-risk pat...
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