AIMC Topic: Radiography, Abdominal
Clear Filters Showing 31 to 40 of 93 articles
Applying Machine Learning Analysis Based on Proximal Femur of Abdominal Computed Tomography to Screen for Abnormal Bone Mass in Femur.
Academic radiology
Nov 15, 2023
RATIONALE AND OBJECTIVES: To evaluate the performance of machine learning analysis based on proximal femur of abdominal computed tomography (CT) scans in screening for abnormal bone mass in femur.
Towards optimal deep fusion of imaging and clinical data via a model-based description of fusion quality.
Medical physics
Jan 7, 2023
BACKGROUND: Due to intrinsic differences in data formatting, data structure, and underlying semantic information, the integration of imaging data with clinical data can be non-trivial. Optimal integration requires robust data fusion, that is, the pro...
Denoising of pediatric low dose abdominal CT using deep learning based algorithm.
PloS one
Jan 21, 2022
OBJECTIVES: To evaluate standard dose-like computed tomography (CT) images generated by a deep learning method, trained using unpaired low-dose CT (LDCT) and standard-dose CT (SDCT) images.
Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction.
BMC medical imaging
Oct 10, 2021
BACKGROUND: Efforts to reduce the radiation dose have continued steadily, with new reconstruction techniques. Recently, image denoising algorithms using artificial neural networks, termed deep learning reconstruction (DLR), have been applied to CT im...
A deep-learning method using computed tomography scout images for estimating patient body weight.
Scientific reports
Aug 2, 2021
Body weight is an indispensable parameter for determination of contrast medium dose, appropriate drug dosing, or management of radiation dose. However, we cannot always determine the accurate patient body weight at the time of computed tomography (CT...
An artificial intelligence deep learning model for identification of small bowel obstruction on plain abdominal radiographs.
The British journal of radiology
Apr 27, 2021
OBJECTIVES: Small bowel obstruction is a common surgical emergency which can lead to bowel necrosis, perforation and death. Plain abdominal X-rays are frequently used as a first-line test but the availability of immediate expert radiological review i...
Deep-learning-based image reconstruction in dynamic contrast-enhanced abdominal CT: image quality and lesion detection among reconstruction strength levels.
Clinical radiology
Apr 18, 2021
AIM: To evaluate the use of deep-learning-based image reconstruction (DLIR) algorithms in dynamic contrast-enhanced computed tomography (CT) of the abdomen, and to compare the image quality and lesion conspicuity among the reconstruction strength lev...
Development and Validation of a Radiomics Model for Differentiating Bone Islands and Osteoblastic Bone Metastases at Abdominal CT.
Radiology
Mar 30, 2021
Background It is important to diagnose sclerotic bone lesions in order to determine treatment strategy. Purpose To evaluate the diagnostic performance of a CT radiomics-based machine learning model for differentiating bone islands and osteoblastic bo...
Visual Analytics of a Computer-Aided Diagnosis System for Pancreatic Lesions.
IEEE transactions on visualization and computer graphics
Jan 28, 2021
Machine learning is a powerful and effective tool for medical image analysis to perform computer-aided diagnosis (CAD). Having great potential in improving the accuracy of a diagnosis, CAD systems are often analyzed in terms of the final accuracy, le...
Deep learning image reconstruction for improvement of image quality of abdominal computed tomography: comparison with hybrid iterative reconstruction.
Japanese journal of radiology
Jan 15, 2021
PURPOSE: To evaluate the usefulness of the deep learning image reconstruction (DLIR) to enhance the image quality of abdominal CT, compared to iterative reconstruction technique.
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