AIMC Topic: Tomography, X-Ray Computed
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W-DRAG: A joint framework of WGAN with data random augmentation optimized for generative networks for bone marrow edema detection in dual energy CT.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Apr 24, 2024
Dual-energy computed tomography (CT) is an excellent substitute for identifying bone marrow edema in magnetic resonance imaging. However, it is rarely used in practice owing to its low contrast. To overcome this problem, we constructed a framework ba...
Longitudinal assessment of interstitial lung abnormalities on CT in patients with COPD using artificial intelligence-based segmentation: a prospective observational study.
BMC pulmonary medicine
Apr 23, 2024
BACKGROUND: Interstitial lung abnormalities (ILAs) on CT may affect the clinical outcomes in patients with chronic obstructive pulmonary disease (COPD), but their quantification remains unestablished. This study examined whether artificial intelligen...
Metabolic phenotyping with computed tomography deep learning for metabolic syndrome, osteoporosis and sarcopenia predicts mortality in adults.
Journal of cachexia, sarcopenia and muscle
Apr 22, 2024
BACKGROUND: Computed tomography (CT) body compositions reflect age-related metabolic derangements. We aimed to develop a multi-outcome deep learning model using CT multi-level body composition parameters to detect metabolic syndrome (MS), osteoporosi...
Hepatic and portal vein segmentation with dual-stream deep neural network.
Medical physics
Apr 22, 2024
BACKGROUND: Liver lesions mainly occur inside the liver parenchyma, which are difficult to locate and have complicated relationships with essential vessels. Thus, preoperative planning is crucial for the resection of liver lesions. Accurate segmentat...
Using an artificial intelligence software improves emergency medicine physician intracranial haemorrhage detection to radiologist levels.
Emergency medicine journal : EMJ
Apr 22, 2024
BACKGROUND: Tools to increase the turnaround speed and accuracy of imaging reports could positively influence ED logistics. The Caire ICH is an artificial intelligence (AI) software developed for ED physicians to recognise intracranial haemorrhages (...
Clinical value of deep learning image reconstruction on the diagnosis of pulmonary nodule for ultra-low-dose chest CT imaging.
Clinical radiology
Apr 20, 2024
PURPOSE: To compare the image quality and pulmonary nodule detectability between deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction-Veo (ASIR-V) in ultra-low-dose CT (ULD-CT).
COVID-19 Hierarchical Classification Using a Deep Learning Multi-Modal.
Sensors (Basel, Switzerland)
Apr 20, 2024
Coronavirus disease 2019 (COVID-19), originating in China, has rapidly spread worldwide. Physicians must examine infected patients and make timely decisions to isolate them. However, completing these processes is difficult due to limited time and ava...
Synthetic Low-Energy Monochromatic Image Generation in Single-Energy Computed Tomography System Using a Transformer-Based Deep Learning Model.
Journal of imaging informatics in medicine
Apr 18, 2024
While dual-energy computed tomography (DECT) technology introduces energy-specific information in clinical practice, single-energy CT (SECT) is predominantly used, limiting the number of people who can benefit from DECT. This study proposed a novel m...
Radiomics and machine learning for renal tumor subtype assessment using multiphase computed tomography in a multicenter setting.
European radiology
Apr 18, 2024
OBJECTIVES: To distinguish histological subtypes of renal tumors using radiomic features and machine learning (ML) based on multiphase computed tomography (CT).
Deep Learning Prediction of Axillary Lymph Node Metastasis in Breast Cancer Patients Using Clinical Implication-Applied Preprocessed CT Images.
Current oncology (Toronto, Ont.)
Apr 18, 2024
Accurate detection of axillary lymph node (ALN) metastases in breast cancer is crucial for clinical staging and treatment planning. This study aims to develop a deep learning model using clinical implication-applied preprocessed computed tomography ...
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