AIMC Topic: Radiography, Abdominal
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Estimating patient-specific organ doses from head and abdominal CT scans via machine learning with optimized regulation strength and feature quantity.
Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine
Mar 15, 2025
PURPOSE: This study aims to investigate estimation of patient-specific organ doses from CT scans via radiomics feature-based SVR models with training parameter optimization, and maximize SVR models' predictive accuracy and robustness via fine-tuning ...
Development and validation of a deep learning algorithm for prediction of pediatric recurrent intussusception in ultrasound images and radiographs.
BMC medical imaging
Mar 3, 2025
PURPOSES: To develop a predictive model for recurrent intussusception based on abdominal ultrasound (US) images and abdominal radiographs.
Artificial Intelligence Model for Detection of Colorectal Cancer on Routine Abdominopelvic CT Examinations: A Training and External-Testing Study.
AJR. American journal of roentgenology
Feb 12, 2025
Radiologists are prone to missing some colorectal cancers (CRCs) on routine abdominopelvic CT examinations that are in fact detectable on the images. The purpose of this study was to develop an artificial intelligence (AI) model to detect CRC on ro...
The impact of the novel CovBat harmonization method on enhancing radiomics feature stability and machine learning model performance: A multi-center, multi-device study.
European journal of radiology
Jan 29, 2025
PURPOSE: This study aims to assess whether the novel CovBat harmonization method can further reduce radiomics feature variability from different imaging devices in multi-center studies and improve machine learning model performance compared to the Co...
Intraindividual Comparison of Image Quality Between Low-Dose and Ultra-Low-Dose Abdominal CT With Deep Learning Reconstruction and Standard-Dose Abdominal CT Using Dual-Split Scan.
Investigative radiology
Jan 28, 2025
OBJECTIVE: The aim of this study was to intraindividually compare the conspicuity of focal liver lesions (FLLs) between low- and ultra-low-dose computed tomography (CT) with deep learning reconstruction (DLR) and standard-dose CT with model-based ite...
Opportunistic AI for enhanced cardiovascular disease risk stratification using abdominal CT scans.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 20, 2025
This study introduces the Deep Learning-based Cardiovascular Disease Incident (DL-CVDi) score, a novel biomarker derived from routine abdominal CT scans, optimized to predict cardiovascular disease (CVD) risk using deep survival learning. CT imaging,...
Image quality assessment of artificial intelligence iterative reconstruction for low dose unenhanced abdomen: comparison with hybrid iterative reconstruction.
Abdominal radiology (New York)
Dec 21, 2024
OBJECTIVES: To assess the impact of artificial intelligence iterative reconstruction algorithms (AIIR) on image quality with phantom and clinical studies.
Reduced-dose deep learning iterative reconstruction for abdominal computed tomography with low tube voltage and tube current.
BMC medical informatics and decision making
Dec 18, 2024
BACKGROUND: The low tube-voltage technique (e.g., 80 kV) can efficiently reduce the radiation dose and increase the contrast enhancement of vascular and parenchymal structures in abdominal CT. However, a high tube current is always required in this s...
Evolving and Novel Applications of Artificial Intelligence in Abdominal Imaging.
Tomography (Ann Arbor, Mich.)
Nov 18, 2024
Advancements in artificial intelligence (AI) have significantly transformed the field of abdominal radiology, leading to an improvement in diagnostic and disease management capabilities. This narrative review seeks to evaluate the current standing of...
Contrast-enhanced thin-slice abdominal CT with super-resolution deep learning reconstruction technique: evaluation of image quality and visibility of anatomical structures.
Japanese journal of radiology
Nov 14, 2024
PURPOSE: To compare image quality and visibility of anatomical structures on contrast-enhanced thin-slice abdominal CT images reconstructed using super-resolution deep learning reconstruction (SR-DLR), deep learning-based reconstruction (DLR), and hy...
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