AI Medical Compendium Topic:
Radiographic Image Interpretation, Computer-Assisted

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Automated body composition analysis of clinically acquired computed tomography scans using neural networks.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND & AIMS: The quantity and quality of skeletal muscle and adipose tissue is an important prognostic factor for clinical outcomes across several illnesses. Clinically acquired computed tomography (CT) scans are commonly used for quantificatio...

Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT.

AJR. American journal of roentgenology
The objective of this study was to compare image quality and clinically significant lesion detection on deep learning reconstruction (DLR) and iterative reconstruction (IR) images of submillisievert chest and abdominopelvic CT. Our prospective mult...

Classification of Interstitial Lung Abnormality Patterns with an Ensemble of Deep Convolutional Neural Networks.

Scientific reports
Subtle interstitial changes in the lung parenchyma of smokers, known as Interstitial Lung Abnormalities (ILA), have been associated with clinical outcomes, including mortality, even in the absence of Interstitial Lung Disease (ILD). Although several ...

Deep learning in fracture detection: a narrative review.

Acta orthopaedica
Artificial intelligence (AI) is a general term that implies the use of a computer to model intelligent behavior with minimal human intervention. AI, particularly deep learning, has recently made substantial strides in perception tasks allowing machin...

Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy.

Journal of cardiovascular computed tomography
BACKGROUND: Advances in image reconstruction are necessary to decrease radiation exposure from coronary CT angiography (CCTA) further, but iterative reconstruction has been shown to degrade image quality at high levels. Deep-learning image reconstruc...

Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering.

Medical image analysis
Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified with X-ray opaque contrast agents. Interventional cardiologists typically navigate instruments using n...

Improving Low-contrast Detectability and Noise Texture Pattern for Computed Tomography Using Iterative Reconstruction Accelerated with Machine Learning Method: A Phantom Study.

Academic radiology
RATIONALE AND OBJECTIVES: To evaluate the performance of iterative reconstruction (IR) and filtered back projection (FBP) images in terms of low-contrast detectability at different radiation doses, IR levels, and slice thickness using the mathematica...

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection.

IEEE journal of biomedical and health informatics
Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung nodules in computed tomography (CT) images for the diagnosis of lung cancer. However, due to the het...

Test-retest reproducibility of a deep learning-based automatic detection algorithm for the chest radiograph.

European radiology
OBJECTIVES: To perform test-retest reproducibility analyses for deep learning-based automatic detection algorithm (DLAD) using two stationary chest radiographs (CRs) with short-term intervals, to analyze influential factors on test-retest variations,...

Semi-Supervised Learning for Semantic Segmentation of Emphysema With Partial Annotations.

IEEE journal of biomedical and health informatics
Segmentation and quantification of each subtype of emphysema is helpful to monitor chronic obstructive pulmonary disease. Due to the nature of emphysema (diffuse pulmonary disease), it is very difficult for experts to allocate semantic labels to ever...