AIMC Topic: Radiographic Image Interpretation, Computer-Assisted

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Automated classification of coronary LEsions fRom coronary computed Tomography angiography scans with an updated deep learning model: ALERT study.

European radiology Jan 10, 2025
OBJECTIVES: The use of deep learning models for quantitative measurements on coronary computed tomography angiography (CCTA) may reduce inter-reader variability and increase efficiency in clinical reporting. This study aimed to investigate the diagno...
Middle Aged Deep Learning Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Coronary Artery Disease Observer Variation Computed Tomography Angiography Coronary Stenosis Female Reproducibility of Results Aged Male Humans Retrospective Studies
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Interpretable CT Radiomics-based Machine Learning Model for Preoperative Prediction of Ki-67 Expression in Clear Cell Renal Cell Carcinoma.

Academic radiology Jan 9, 2025
RATIONALE AND OBJECTIVES: To develop and externally validate interpretable CT radiomics-based machine learning (ML) models for preoperative Ki-67 expression prediction in clear cell renal cell carcinoma (ccRCC).
Middle Aged Radiographic Image Interpretation, Computer-Assisted Kidney Neoplasms Carcinoma, Renal Cell Aged Adult Radiomics Male Ki-67 Antigen Humans Machine Learning Retrospective Studies Tomography, X-Ray Computed Female
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Traditional versus modern approaches to screening mammography: a comparison of computer-assisted detection for synthetic 2D mammography versus an artificial intelligence algorithm for digital breast tomosynthesis.

Breast cancer research and treatment Jan 9, 2025
PURPOSE: Traditional computer-assisted detection (CADe) algorithms were developed for 2D mammography, while modern artificial intelligence (AI) algorithms can be applied to 2D mammography and/or digital breast tomosynthesis (DBT). The objective is to...
Female Aged Adult Breast Neoplasms Humans Artificial Intelligence Algorithms Mammography Middle Aged Radiographic Image Interpretation, Computer-Assisted Deep Learning ROC Curve Early Detection of Cancer
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Automated stenosis estimation of coronary angiographies using end-to-end learning.

The international journal of cardiovascular imaging Jan 9, 2025
The initial evaluation of stenosis during coronary angiography is typically performed by visual assessment. Visual assessment has limited accuracy compared to fractional flow reserve and quantitative coronary angiography, which are more time-consumin...
Fractional Flow Reserve, Myocardial Deep Learning Predictive Value of Tests Coronary Angiography Radiographic Image Interpretation, Computer-Assisted Aged Humans Male Coronary Artery Disease Retrospective Studies Middle Aged Severity of Illness Index Automation Female Reproducibility of Results Coronary Vessels Coronary Stenosis
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G-SET-DCL: a guided sequential episodic training with dual contrastive learning approach for colon segmentation.

International journal of computer assisted radiology and surgery Jan 9, 2025
PURPOSE: This article introduces a novel deep learning approach to substantially improve the accuracy of colon segmentation even with limited data annotation, which enhances the overall effectiveness of the CT colonography pipeline in clinical settin...
Radiographic Image Interpretation, Computer-Assisted Colonic Polyps Colonography, Computed Tomographic Reproducibility of Results Colon Deep Learning Humans Imaging, Three-Dimensional Algorithms
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Predicting mortality after transcatheter aortic valve replacement using AI-based fully automated left atrioventricular coupling index.

Journal of cardiovascular computed tomography Jan 9, 2025
BACKGROUND: This study aimed to determine whether artificial intelligence (AI)-based automated assessment of left atrioventricular coupling index (LACI) can provide incremental value above other traditional risk factors for predicting mortality among...
Transcatheter Aortic Valve Replacement Risk Assessment Severity of Illness Index Time Factors Treatment Outcome Humans Retrospective Studies Radiographic Image Interpretation, Computer-Assisted Aortic Valve Stenosis Artificial Intelligence Aged, 80 and over Female Aortic Valve Automation Male Risk Factors Predictive Value of Tests Atrial Function, Left Ventricular Function, Left Aged Coronary Angiography Computed Tomography Angiography Stroke Volume
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Repeatability of AI-based, automatic measurement of vertebral and cardiovascular imaging biomarkers in low-dose chest CT: the ImaLife cohort.

European radiology Jan 8, 2025
OBJECTIVE: To evaluate the repeatability of AI-based automatic measurement of vertebral and cardiovascular markers on low-dose chest CT.
Artificial Intelligence Biomarkers Tomography, X-Ray Computed Female Humans Reproducibility of Results Cohort Studies Aged Male Radiographic Image Interpretation, Computer-Assisted Middle Aged Radiation Dosage Radiography, Thoracic
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Enhanced swin transformer based tuberculosis classification with segmentation using chest X-ray.

Journal of X-ray science and technology Jan 8, 2025
BACKGROUND:: Tuberculosis disease is the disease that causes significant morbidity and mortality worldwide. Thus, early detection of the disease is crucial for proper treatment and controlling the spread of Tuberculosis disease. Chest X-ray imaging i...
Tuberculosis, Pulmonary Humans Deep Learning Radiography, Thoracic Neural Networks, Computer Image Processing, Computer-Assisted Algorithms Radiographic Image Interpretation, Computer-Assisted Tuberculosis
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Sparse keypoint segmentation of lung fissures: efficient geometric deep learning for abstracting volumetric images.

International journal of computer assisted radiology and surgery Jan 7, 2025
PURPOSE: Lung fissure segmentation on CT images often relies on 3D convolutional neural networks (CNNs). However, 3D-CNNs are inefficient for detecting thin structures like the fissures, which make up a tiny fraction of the entire image volume. We pr...
Tomography, X-Ray Computed Lung Deep Learning Imaging, Three-Dimensional Radiographic Image Interpretation, Computer-Assisted Humans Neural Networks, Computer
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DCTP-Net: Dual-Branch CLIP-Enhance Textual Prompt-Aware Network for Acute Ischemic Stroke Lesion Segmentation From CT Image.

IEEE journal of biomedical and health informatics Jan 7, 2025
Detecting early ischemic lesions (EIL) in computed tomography (CT) images is crucial for reducing diagnostic time and minimizing neuron loss due to oxygen deprivation. This paper introduces DCTP-Net, a dual-branch network for segmenting acute ischemi...
Tomography, X-Ray Computed Ischemic Stroke Radiographic Image Interpretation, Computer-Assisted Humans Neural Networks, Computer Brain
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