BACKGROUND: Fatty liver disease is a common condition linked to metabolic syndrome, cardiovascular diseases, and liver cirrhosis, and timely, accurate diagnosis is crucial. In clinical studies, incorporating deep learning models often faces the chall...
Deep vein thrombosis (DVT) in fracture patients is often clinically silent, with a high incidence of thrombosis and associated mortality. Static machine learning methods struggle to address the challenge of early DVT diagnosis due to their inability ...
Biomedical physics & engineering express
Jan 6, 2026
. To classify digital mammograms based on radiological findings using morphology and texture descriptors with artificial neural networks (ANN) for breast cancer detection.The mammography dataset from High Specialty Regional Hospital of Oaxaca (HRAEO)...
The incidence of allergic rhinitis (AR) has been increasing annually, severely impacting patients' quality of life and increasing socioeconomic burdens. The limitations of current diagnostic methods have made the development of efficient, low-cost ea...
The classification of malignant versus benign microcalcifications in mammograms remains a critical yet challenging task in breast cancer screening. Deep learning models, particularly convolutional neural networks, have demonstrated promising results;...
Machine learning (ML) has the potential to drastically improve clinical decision-making by predicting diseases early, accurately, and based on data. This study evaluated and compared the performance of several machine learning models, including a fee...
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...
PURPOSE: Intestinal obstruction surgery is a high-risk procedure associated with postoperative sepsis. In this multicenter retrospective study, we aimed to employ machine-learning methods to predict sepsis after intestinal obstruction surgery and vis...
BACKGROUND: Renal injury is a severe complication among individuals diagnosed with gout. This research constructed a machine learning predictive model to assess renal injury risk in gout patients.
BACKGROUND: The clinical diagnosis, severity assessment, and outcome prognostication of community-acquired pneumonia (CAP) remain challenging due to the complex disease pathophysiology. Accurate outcome prediction is crucial for optimizing patient ma...
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