AIMC Topic: Radiography, Abdominal
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Prospective Evaluation of Artificial Intelligence Triage of Incidental Pulmonary Emboli on Contrast-Enhanced CT Examinations of the Chest or Abdomen.
AJR. American journal of roentgenology
Jun 20, 2024
Artificial intelligence (AI) algorithms improved detection of incidental pulmonary embolism (IPE) on contrast-enhanced CT (CECT) examinations in retrospective studies; however, prospective validation studies are lacking. The purpose of this study w...
Artificial Intelligence vs. Doctors: Diagnosing Necrotizing Enterocolitis on Abdominal Radiographs.
Journal of pediatric surgery
Jun 8, 2024
BACKGROUND: Radiographic diagnosis of necrotizing enterocolitis (NEC) is challenging. Deep learning models may improve accuracy by recognizing subtle imaging patterns. We hypothesized it would perform with comparable accuracy to that of senior surgic...
Low-iodine-dose computed tomography coupled with an artificial intelligence-based contrast-boosting technique in children: a retrospective study on comparison with conventional-iodine-dose computed tomography.
Pediatric radiology
Jun 5, 2024
BACKGROUND: Low-iodine-dose computed tomography (CT) protocols have emerged to mitigate the risks associated with contrast injection, often resulting in decreased image quality.
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.
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...
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.
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...
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 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...
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.
Aged, 80 and over
Adult
Humans
Sensitivity and Specificity
Colorectal Neoplasms
Middle Aged
Tomography, X-Ray Computed
Neural Networks, Computer
Male
Retrospective Studies
Female
Adenocarcinoma
Reproducibility of Results
Radiographic Image Interpretation, Computer-Assisted
Aged
Radiography, Abdominal
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