AIMC Topic: Radiography, Abdominal

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Low tube voltage and deep-learning reconstruction for reducing radiation and contrast medium doses in thin-slice abdominal CT: a prospective clinical trial.

European radiology May 16, 2024
OBJECTIVES: To investigate the feasibility of low-radiation dose and low iodinated contrast medium (ICM) dose protocol combining low-tube voltage and deep-learning reconstruction (DLR) algorithm in thin-slice abdominal CT.
Radiography, Abdominal Aged, 80 and over Prospective Studies Radiographic Image Interpretation, Computer-Assisted Adult Tomography, X-Ray Computed Deep Learning Male Female Contrast Media Feasibility Studies Aged Middle Aged Radiation Dosage Humans Algorithms
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Deep Learning Models for Abdominal CT Organ Segmentation in Children: Development and Validation in Internal and Heterogeneous Public Datasets.

AJR. American journal of roentgenology May 1, 2024
Deep learning abdominal organ segmentation algorithms have shown excellent results in adults; validation in children is sparse. The purpose of this article is to develop and validate deep learning models for liver, spleen, and pancreas segmentation...
Female Infant, Newborn Male Liver Infant Datasets as Topic Humans Pancreas Retrospective Studies Spleen Tomography, X-Ray Computed Radiography, Abdominal Deep Learning Adolescent Child, Preschool Child
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Automated abdominal CT contrast phase detection using an interpretable and open-source artificial intelligence algorithm.

European radiology Apr 29, 2024
OBJECTIVES: To develop and validate an open-source artificial intelligence (AI) algorithm to accurately detect contrast phases in abdominal CT scans.
Aged Male Middle Aged Deep Learning Algorithms Retrospective Studies Tomography, X-Ray Computed Female Contrast Media Radiographic Image Interpretation, Computer-Assisted Radiography, Abdominal Humans Artificial Intelligence
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Fully automated explainable abdominal CT contrast media phase classification using organ segmentation and machine learning.

Medical physics Apr 17, 2024
BACKGROUND: Contrast-enhanced computed tomography (CECT) provides much more information compared to non-enhanced CT images, especially for the differentiation of malignancies, such as liver carcinomas. Contrast media injection phase information is us...
Machine Learning Tomography, X-Ray Computed Image Processing, Computer-Assisted Abdomen Contrast Media Humans Automation Radiography, Abdominal
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Detection of urinary tract stones on submillisievert abdominopelvic CT imaging with deep-learning image reconstruction algorithm (DLIR).

Abdominal radiology (New York) Mar 12, 2024
PURPOSE: Urolithiasis is a chronic condition that leads to repeated CT scans throughout the patient's life. The goal was to assess the diagnostic performance and image quality of submillisievert abdominopelvic computed tomography (CT) using deep lear...
Deep Learning Radiographic Image Interpretation, Computer-Assisted Radiation Dosage Radiography, Abdominal Urolithiasis Urinary Calculi Adult Sensitivity and Specificity Male Middle Aged Tomography, X-Ray Computed Female Aged Aged, 80 and over Humans Algorithms Retrospective Studies
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Deep learning for automatic bowel-obstruction identification on abdominal CT.

European radiology Feb 22, 2024
RATIONALE AND OBJECTIVES: Automated evaluation of abdominal computed tomography (CT) scans should help radiologists manage their massive workloads, thereby leading to earlier diagnoses and better patient outcomes. Our objective was to develop a machi...
Male Middle Aged Deep Learning Adolescent Radiography, Abdominal Radiographic Image Interpretation, Computer-Assisted Intestinal Obstruction Aged Aged, 80 and over Adult Sensitivity and Specificity Humans Neural Networks, Computer Tomography, X-Ray Computed Female
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Development and multicenter validation of deep convolutional neural network-based detection of colorectal cancer on abdominal CT.

European radiology Feb 1, 2024
OBJECTIVES: This study aims to develop computer-aided detection (CAD) for colorectal cancer (CRC) using abdominal CT based on a deep convolutional neural network.
Humans Neural Networks, Computer Colorectal Neoplasms Male Middle Aged Aged Retrospective Studies Aged, 80 and over Adenocarcinoma Radiographic Image Interpretation, Computer-Assisted Adult Tomography, X-Ray Computed Sensitivity and Specificity Radiography, Abdominal Female Reproducibility of Results
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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.
Machine Learning Male Tomography, X-Ray Computed Humans Bone Density Absorptiometry, Photon Middle Aged Female Retrospective Studies Femur Aged Osteoporosis Radiography, Abdominal Sensitivity and Specificity
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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...
Hypertension Humans Liver Neural Networks, Computer Radiography, Abdominal Tomography, X-Ray Computed
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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 Processing, Computer-Assisted Male Radiography, Abdominal Abdomen Humans Deep Learning Radiation Dosage Child, Preschool Algorithms Signal-To-Noise Ratio Child Tomography, X-Ray Computed Female
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