PURPOSE: To develop a non-invasive auxiliary assessment method based on CT-derived extracellular volume (ECV) to predict the pathological grading (PG) of hepatocellular carcinoma (HCC).
OBJECTIVES: Differentiating intestinal tuberculosis (ITB) from Crohn's disease (CD) remains a diagnostic dilemma. Misdiagnosis carries potential grave implications. We aim to establish a multidisciplinary-based model using machine learning approach f...
Arab journal of gastroenterology : the official publication of the Pan-Arab Association of Gastroenterology
May 4, 2024
BACKGROUND AND STUDY AIMS: The present study was undertaken to design a new machine learning (ML) model that can predict the presence of viremia in hepatitis C virus (HCV) antibody (anti-HCV) seropositive cases.
BACKGROUND: Although vascularized bone graft (VBG) transfer is the current standard for mandibular reconstruction, reconstruction with a mandibular reconstruction plate (MRP) and with a soft-tissue flap (STF) alone remain crucial options for patients...
PURPOSE: To develop deep learning (DL) algorithm to detect glaucoma progression using optical coherence tomography (OCT) images, in the absence of a reference standard.
RATIONALE AND OBJECTIVES: To develop and validate a deep learning radiomics (DLR) model based on contrast-enhanced computed tomography (CT) to identify the primary source of liver metastases.
BACKGROUND: Rectal tumors display varying degrees of response to total neoadjuvant therapy (TNT). We evaluated the performance of a convolutional neural network (CNN) in interpreting endoscopic images of either a non-complete response to TNT or local...
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
May 3, 2024
PURPOSE: To investigate the possibility of distinguishing between IgG4-related ophthalmic disease (IgG4-ROD) and orbital MALT lymphoma using artificial intelligence (AI) and hematoxylin-eosin (HE) images.
AIMS: Electronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. ...
AIM: This study aimed to develop highly precise radiomics and deep learning models to accurately detect acute lymphoblastic leukemia (ALL) using a T1WI image.
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