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Prediction of sepsis among patients with major trauma using artificial intelligence: a multicenter validated cohort study.
International journal of surgery (London, England)
Jan 1, 2025
BACKGROUND: Sepsis remains a significant challenge in patients with major trauma in the ICU. Early detection and treatment are crucial for improving outcomes and reducing mortality rates. Nonetheless, clinical tools for predicting sepsis among patien...
Implementing an AI algorithm in the clinical setting: a case study for the accuracy paradox.
European radiology
Dec 31, 2024
OBJECTIVES: We report our experience implementing an algorithm for the detection of large vessel occlusion (LVO) for suspected stroke in the emergency setting, including its performance, and offer an explanation as to why it was poorly received by ra...
Predicting Discharge Destination From Inpatient Rehabilitation Using Machine Learning.
American journal of physical medicine & rehabilitation
Dec 31, 2024
Predicting discharge destination for patients at inpatient rehabilitation facilities is important as it facilitates transitions of care and can improve healthcare resource utilization. This study aims to build on previous studies investigating discha...
Machine Learning-Based Pathomics Model Predicts Angiopoietin-2 Expression and Prognosis in Hepatocellular Carcinoma.
The American journal of pathology
Dec 31, 2024
Angiopoietin-2 (ANGPT2) shows promise as prognostic marker and therapeutic target in hepatocellular carcinoma (HCC). However, assessing ANGPT2 expression and prognostic potential using histopathology images viewed with naked eye is challenging. Herei...
Knee osteoarthritis severity detection using deep inception transfer learning.
Computers in biology and medicine
Dec 31, 2024
Osteoarthritis (OA) is a prevalent condition resulting in physical limitations. Early detection of OA is critical to effectively manage this condition. However, the diagnosis of early-stage arthritis remains challenging. The Kellgren and Lawrence (KL...
Computerized classification method for significant coronary artery stenosis on whole-heart coronary MRA using 3D convolutional neural networks with attention mechanisms.
Radiological physics and technology
Dec 31, 2024
This study aims to develop a computerized classification method for significant coronary artery stenosis on whole-heart coronary magnetic resonance angiography (WHCMRA) images using a 3D convolutional neural network (3D-CNN) with attention mechanisms...
Validation of AI-driven measurements for hip morphology assessment.
European journal of radiology
Dec 31, 2024
RATIONALE AND OBJECTIVES: Accurate assessment of hip morphology is crucial for the diagnosis and management of hip pathologies. Traditional manual measurements are prone to mistakes and inter- and intra-reader variability. Artificial intelligence (AI...
Using Machine Learning to Predict Weight Gain in Adults: an Observational Analysis From the All of Us Research Program.
The Journal of surgical research
Dec 31, 2024
INTRODUCTION: Obesity, defined as a body mass index ≥30 kg/m, is a major public health concern in the United States. Preventative approaches are essential, but they are limited by an inability to accurately predict individuals at highest risk of weig...
Exploring machine learning tools in a retrospective case-study of patients with metastatic non-small cell lung cancer treated with first-line immunotherapy: A feasibility single-centre experience.
Lung cancer (Amsterdam, Netherlands)
Dec 31, 2024
BACKGROUND: Artificial intelligence (AI) models are emerging as promising tools to identify predictive features among data coming from health records. Their application in clinical routine is still challenging, due to technical limits and to explaina...
Deep learning enhanced transmembranous electromyography in the diagnosis of sleep apnea.
BMC neuroscience
Dec 31, 2024
Obstructive sleep apnea (OSA) is widespread, under-recognized, and under-treated, impacting the health and quality of life for millions. The current gold standard for sleep apnea testing is based on the in-lab sleep study, which is costly, cumbersome...
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