AIMC Topic: Radiography, Abdominal
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A Pilot Study on Using an Artificial Intelligence Algorithm to Identify Urolith Composition through Abdominal Radiographs in the Dog.
Veterinary radiology & ultrasound : the official journal of the American College of Veterinary Radiology and the International Veterinary Radiology Association
Mar 1, 2025
In small animal practice, patients often present with urinary lithiasis, and prediction of urolith composition is essential to determine the appropriate treatment. Through abdominal radiographs, the composition of mineral radiopaque uroliths can be d...
Applying Artificial Intelligence to Quantify Body Composition on Abdominal CTs and Better Predict Kidney Transplantation Wait-List Mortality.
Journal of the American College of Radiology : JACR
Mar 1, 2025
BACKGROUND: Prekidney transplant evaluation routinely includes abdominal CT for presurgical vascular assessment. A wealth of body composition data are available from these CT examinations, but they remain an underused source of data, often missing fr...
State-of-the-Art Deep Learning CT Reconstruction Algorithms in Abdominal Imaging.
Radiographics : a review publication of the Radiological Society of North America, Inc
Dec 1, 2024
The implementation of deep neural networks has spurred the creation of deep learning reconstruction (DLR) CT algorithms. DLR CT techniques encompass a spectrum of deep learning-based methodologies that operate during the different steps of the image ...
External validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on abdominal CT images.
The British journal of radiology
Dec 1, 2024
OBJECTIVES: Body composition assessment using CT images at the L3-level is increasingly applied in cancer research and has been shown to be strongly associated with long-term survival. Robust high-throughput automated segmentation is key to assess la...
[Deep Learning Reconstruction Algorithm Combined With Smart Metal Artifact Reduction Technique Improves Image Quality of Upper Abdominal CT in Critically Ill Patients].
Sichuan da xue xue bao. Yi xue ban = Journal of Sichuan University. Medical science edition
Nov 20, 2024
OBJECTIVE: To evaluate the effect of deep learning reconstruction algorithm combined with smart metal artifact reduction (DLMAR) on the quality of abdominal CT images in critically ill patients who are unable to raise their arms and require electroca...
Deep Learning Reconstruction in Abdominopelvic Contrast-Enhanced CT for The Evaluation of Hemorrhages.
Radiologic technology
Nov 1, 2024
PURPOSE: To investigate the effects of deep learning reconstruction on depicting arteries and providing suitable images for the evaluation of hemorrhages with abdominopelvic contrast-enhanced computed tomography (CT) compared with hybrid iterative re...
Diagnosing Necrotizing Enterocolitis via Fine-Grained Visual Classification.
IEEE transactions on bio-medical engineering
Nov 1, 2024
Necrotizing Enterocolitis (NEC) is a devastating condition affecting prematurely born neonates. Reviewing Abdominal X-rays (AXRs) is a key step in NEC diagnosis, staging and treatment decision-making, but poses significant challenges due to the subtl...
Development and validation of deep learning models for bowel obstruction on plain abdominal radiograph.
The Journal of international medical research
Sep 1, 2024
OBJECTIVE: Artificial intelligence (AI) could help medical practitioners in analyzing radiological images to determine the presence and site of bowel obstruction. This retrospective diagnostic study proposed a series of deep learning (DL) models for ...
Deep Learning-Enabled Detection of Pneumoperitoneum in Supine and Erect Abdominal Radiography: Modeling Using Transfer Learning and Semi-Supervised Learning.
Korean journal of radiology
Jun 1, 2023
OBJECTIVE: Detection of pneumoperitoneum using abdominal radiography, particularly in the supine position, is often challenging. This study aimed to develop and externally validate a deep learning model for the detection of pneumoperitoneum using sup...
Clinical Artificial Intelligence Applications in Radiology: Chest and Abdomen.
Radiologic clinics of North America
Nov 1, 2021
Organ segmentation, chest radiograph classification, and lung and liver nodule detections are some of the popular artificial intelligence (AI) tasks in chest and abdominal radiology due to the wide availability of public datasets. AI algorithms have ...
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