To develop a deep learning (DL) model based on MRI to predict muscle-invasive bladder cancer (MIBC). A total of 559 patients, including 521 patients in our center and 38 patients in external centers were collected from 2012 to 2023 to construct the D...
OBJECTIVE: Substance use disorder (SUD) is clinically under-detected and under-documented. We built and validated machine learning (ML) models to estimate SUD prevalence from electronic health record (EHR) data and to assess variation in facility-lev...
BACKGROUND: Ischemic stroke (IS) is one of the most common causes of disability in adults worldwide. This study aimed to identify key genes related to the inflammatory response to provide insights into the mechanisms and management of IS.
pressure injuries are significant concern for ICU patients on mechanical ventilation. Early prediction is crucial for enhancing patient outcomes and reducing healthcare costs. This study aims to develop a predictive model using machine learning techn...
Computational pathology has primarily focused on analyzing tissue slides, neglecting the valuable information contained in gross images. To bridge this gap, we proposed a novel approach leveraging the Swin Transformer architecture to develop a Swin-T...
PURPOSE: We aims to assessed and compare four deep learning(DL) models using non-contrast-enhanced magnetic resonance imaging(MRI) to differentiate benign from malignant ovarian tumors, considering diagnostic efficacy and associated development costs...
European journal of gastroenterology & hepatology
Mar 21, 2025
BACKGROUND AND AIMS: Chronic hepatitis B poses a major health risk, especially its progression to decompensated cirrhosis. Early prediction is crucial for better outcomes. This study evaluated the predictive power of Golgi protein 73 (GP73), α1-micro...
BACKGROUND: The almond moth, Cadra cautella (Walker), is a significant pest of stored products globally, causing severe damage and contamination. This insect was reported to have attraction towards light and this phenomenon can be exploited for its m...
We investigated the generalizability of a machine learning model trained to predict Kawasaki disease using laboratory and clinical data. The algorithm performed with >89% accuracy at 3 children's hospitals across the United States, demonstrating its ...
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