Journal of orthopaedic surgery and research
Nov 28, 2024
BACKGROUND: Machine learning (ML) has been widely applied to predict the outcomes of numerous diseases. The current study aimed to develop a prognostic prediction model using machine learning algorithms and identify risk factors associated with resid...
Stroke is a neurological complication that can occur in patients admitted to the intensive care unit (ICU) for non-neurological conditions, leading to increased mortality and prolonged hospital stays. The incidence of stroke in ICU settings is notabl...
Fluid overload is associated with increased morbidity and mortality after pediatric cardiac surgery. Management of fluid overload can be difficult and conventional tools may increase the risk of acute kidney injury. This study aimed to study the effe...
PURPOSE: To develop and validate a pachymetry-based machine learning (ML) index for differentiating keratoconus, keratoconus suspect, and normal corneas.
European journal of nuclear medicine and molecular imaging
Nov 27, 2024
PURPOSE: Deep convolutional neural networks (CNN) hold promise for assisting the interpretation of dopamine transporter (DAT)-SPECT. For improved communication of uncertainty to the user it is crucial to reliably discriminate certain from inconclusiv...
Journal of X-ray science and technology
Nov 27, 2024
BACKGROUD: Schwannoma (SCH) and meningiomas (MEN) are the two most common primary spinal cord tumors. Differentiating between them preoperatively remains a clinical challenge due to the substantial overlap in their clinical presentation and imaging c...
PURPOSE: In this study, we aimed to investigate the clinical feasibility of deep learning (DL)-based reconstruction applied to conventional diffusion-weighted imaging (cDWI) and synthetic diffusion-weighted imaging (sDWI) by comparing the DL reconstr...
Cardiovascular and interventional radiology
Nov 27, 2024
PURPOSE: To predict survival and tumor recurrence following image-guided thermal ablation (IGTA) of lung tumors segmented using a deep learning approach.
INTRODUCTION: Patient body composition (BC) has been shown to help predict clinical outcomes in rectal cancer patients. Artificial intelligence algorithms have allowed for easier acquisition of BC measurements, creating a comprehensive BC profile in ...
BACKGROUND: This study aims to develop habitat radiomic models to predict overall survival (OS) for hepatocellular carcinoma (HCC), based on the characterization of the intratumoral heterogeneity reflected in F-FDG PET/CT images.
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