Cardiovascular engineering and technology
Jun 19, 2019
PURPOSE: An abdominal aortic aneurysm (AAA) is known as a cardiovascular disease involving localized deformation (swelling or enlargement) of aorta occurring between the renal and iliac arteries. AAA would jeopardize patients' lives due to its ruptur...
Cerebrovascular diseases (Basel, Switzerland)
Jun 19, 2019
Computed tomography angiography (CTA) collateral scoring can identify patients most likely to benefit from mechanical thrombectomy and those more likely to have good outcomes and ranges from 0 (no collaterals) to 3 (complete collaterals). In this stu...
Background Intratumor heterogeneity in lung cancer may influence outcomes. CT radiomics seeks to assess tumor features to provide detailed imaging features. However, CT radiomic features vary according to the reconstruction kernel used for image gene...
Background Computational models on the basis of deep neural networks are increasingly used to analyze health care data. However, the efficacy of traditional computational models in radiology is a matter of debate. Purpose To evaluate the accuracy and...
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jun 14, 2019
Cardiac motion artifacts frequently reduce the interpretability of coronary computed tomography angiography (CCTA) images and potentially lead to misinterpretations or preclude the diagnosis of coronary artery disease (CAD). In this paper, a novel mo...
Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date back to the 1960s, and in the subsequent years, the main ...
AIM: To assess the ability of artificial neural networks (ANNs) to predict the likelihood of malignancy of pure ground-glass opacities (GGOs), using observations from computed tomography (CT) and 2-[F]-fluoro-2-deoxy-d-glucose (FDG) positron-emission...
A novel method to detect and classify several classes of diseased and healthy lung tissue in CT (Computed Tomography), based on the fusion of Riesz and deep learning features, is presented. First, discriminative parametric lung tissue texture signatu...
AIM: To evaluate the effect of augmented training datasets in a deep convolutional neural network (DCNN) used for detecting abnormal chest radiographs.
Over the past decade, deep learning has demonstrated superior performances in solving many problems in various fields of medicine compared with other machine learning methods. To understand how deep learning has surpassed traditional machine learning...