AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Fully automated artificial intelligence-based coronary CT angiography image processing: efficiency, diagnostic capability, and risk stratification.
European radiology
Jan 9, 2024
OBJECTIVES: To prospectively investigate whether fully automated artificial intelligence (FAAI)-based coronary CT angiography (CCTA) image processing is non-inferior to semi-automated mode in efficiency, diagnostic ability, and risk stratification of...
Deep-learning reconstruction with low-contrast media and low-kilovoltage peak for CT of the liver.
Clinical radiology
Jan 6, 2024
AIM: To compare images using reduced CM, low-kVp scanning and DLR reconstruction with conventional images (no CM reduction, normal tube voltage, reconstructed with HBIR. To compare images using reduced contrast media (CM), low kilovoltage peak (kVp) ...
Deep Learning-Based CT Noise Reduction for Perivascular Adipose Tissue Evaluation.
Academic radiology
Jan 4, 2024
CheXNet and feature pyramid network: a fusion deep learning architecture for multilabel chest X-Ray clinical diagnoses classification.
The international journal of cardiovascular imaging
Dec 27, 2023
The existing multilabel X-Ray image learning tasks generally contain much information on pathology co-occurrence and interdependency, which is very important for clinical diagnosis. However, the challenging part of this subject is to accurately diagn...
Exploring the impact of super-resolution deep learning on MR angiography image quality.
Neuroradiology
Dec 27, 2023
PURPOSE: The aim of this study is to assess the effect of super-resolution deep learning-based reconstruction (SR-DLR), which uses k-space properties, on image quality of intracranial time-of-flight (TOF) magnetic resonance angiography (MRA) at 3 T.
Deep learning for malignancy risk estimation of incidental sub-centimeter pulmonary nodules on CT images.
European radiology
Dec 20, 2023
OBJECTIVES: To establish deep learning models for malignancy risk estimation of sub-centimeter pulmonary nodules incidentally detected by chest CT and managed in clinical settings.
Towards safer imaging: A comparative study of deep learning-based denoising and iterative reconstruction in intraindividual low-dose CT scans using an in-vivo large animal model.
European journal of radiology
Dec 19, 2023
PURPOSE: Computed tomography (CT) scans are a significant source of medically induced radiation exposure. Novel deep learning-based denoising (DLD) algorithms have been shown to enable diagnostic image quality at lower radiation doses than iterative ...
Machine Learning Methods Based on CT Features Differentiate G1/G2 From G3 Pancreatic Neuroendocrine Tumors.
Academic radiology
Dec 4, 2023
RATIONALE AND OBJECTIVES: To identify CT features for distinguishing grade 1 (G1)/grade 2 (G2) from grade 3 (G3) pancreatic neuroendocrine tumors (PNETs) using different machine learning (ML) methods.
Support Vector Machine
Diagnosis, Differential
Pancreatic Neoplasms
Sensitivity and Specificity
Male
Middle Aged
Neoplasm Grading
Radiographic Image Interpretation, Computer-Assisted
Neuroendocrine Tumors
Machine Learning
Algorithms
Retrospective Studies
Aged
Adult
Tomography, X-Ray Computed
Humans
Female
Enhancing cardiac CT imaging quality: Precision metrics for assessing image quality for AI-powered reconstructions.
Diagnostic and interventional imaging
Dec 4, 2023
Retrospectively assessing evaluation and management of artificial-intelligence detected nodules on uninterpreted chest radiographs in the era of radiologists shortage.
European journal of radiology
Nov 28, 2023
PURPOSE: High volumes of chest radiographs (CXR) remain uninterpreted due to severe shortage of radiologists. These CXRs may be informally reported by non-radiologist physicians, or not reviewed at all. Artificial intelligence (AI) software can aid l...
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