AIMC Topic: Tomography, X-Ray Computed
Clear Filters Showing 581 to 590 of 4963 articles
Deep learning model for low-dose CT late iodine enhancement imaging and extracellular volume quantification.
European radiology
Dec 20, 2024
OBJECTIVES: To develop and validate deep learning (DL)-models that denoise late iodine enhancement (LIE) images and enable accurate extracellular volume (ECV) quantification.
Humans
Retrospective Studies
Tomography, X-Ray Computed
Female
Iodine
Contrast Media
Radiographic Image Interpretation, Computer-Assisted
Radiation Dosage
Signal-To-Noise Ratio
Aged
Male
Middle Aged
Deep Learning
Computed Tomography Angiography
Myocardial Perfusion Imaging
Radiographic Image Enhancement
AI model using CT-based imaging biomarkers to predict hepatocellular carcinoma in patients with chronic hepatitis B.
Journal of hepatology
Dec 20, 2024
BACKGROUND & AIMS: Various hepatocellular carcinoma (HCC) prediction models have been proposed for patients with chronic hepatitis B (CHB) using clinical variables. We aimed to develop an artificial intelligence (AI)-based HCC prediction model by inc...
Assessing the prognostic impact of body composition phenotypes on surgical outcomes and survival in patients with spinal metastasis: a deep learning approach to preoperative CT analysis.
Journal of neurosurgery. Spine
Dec 20, 2024
OBJECTIVE: The prognostic significance of body composition phenotypes for survival in patients undergoing surgical intervention for spinal metastases has not yet been elucidated. This study aimed to elucidate the impact of body composition phenotypes...
Automated Identification of Breast Cancer Relapse in Computed Tomography Reports Using Natural Language Processing.
JCO clinical cancer informatics
Dec 20, 2024
PURPOSE: Breast cancer relapses are rarely collected by cancer registries because of logistical and financial constraints. Hence, we investigated natural language processing (NLP), enhanced with state-of-the-art deep learning transformer tools and la...
Artificial Intelligence-Empowered Multistep Integrated Radiation Therapy Workflow for Nasopharyngeal Carcinoma.
International journal of radiation oncology, biology, physics
Dec 19, 2024
PURPOSE: To establish an artificial intelligence (AI)-empowered multistep integrated (MSI) radiation therapy (RT) workflow for patients with nasopharyngeal carcinoma (NPC) and evaluate its feasibility and clinical performance.
Female
Aged
Young Adult
Adult
Male
Middle Aged
Humans
Quality of Life
Radiotherapy, Intensity-Modulated
Artificial Intelligence
Workflow
Nasopharyngeal Carcinoma
Prospective Studies
Feasibility Studies
Organs at Risk
Nasopharyngeal Neoplasms
Tomography, X-Ray Computed
Radiotherapy Planning, Computer-Assisted
Optimizing convolutional neural networks for Chronic Obstructive Pulmonary Disease detection in clinical computed tomography imaging.
Computers in biology and medicine
Dec 19, 2024
We aim to optimize the binary detection of Chronic Obstructive Pulmonary Disease (COPD) based on emphysema presence in the lung with convolutional neural networks (CNN) by exploring manually adjusted versus automated window-setting optimization (WSO)...
A multimodal deep-learning model based on multichannel CT radiomics for predicting pathological grade of bladder cancer.
Abdominal radiology (New York)
Dec 18, 2024
OBJECTIVE: To construct a predictive model using deep-learning radiomics and clinical risk factors for assessing the preoperative histopathological grade of bladder cancer according to computed tomography (CT) images.
Radiomics and deep learning features of pericoronary adipose tissue on non-contrast computerized tomography for predicting non-calcified plaques.
Journal of X-ray science and technology
Dec 18, 2024
BACKGROUND: Inflammation of coronary arterial plaque is considered a key factor in the development of coronary heart disease. Early the plaque detection and timely treatment of the atherosclerosis could effectively reduce the risk of cardiovascular e...
Noise Reduction in Brain CT: A Comparative Study of Deep Learning and Hybrid Iterative Reconstruction Using Multiple Parameters.
Tomography (Ann Arbor, Mich.)
Dec 18, 2024
OBJECTIVES: We evaluated the noise reduction effects of deep learning reconstruction (DLR) and hybrid iterative reconstruction (HIR) in brain computed tomography (CT).
Improving the prediction of patient survival with the aid of residual convolutional neural network (ResNet) in colorectal cancer with unresectable liver metastases treated with bevacizumab-based chemotherapy.
Cancer imaging : the official publication of the International Cancer Imaging Society
Dec 18, 2024
BACKGROUND: To verify overall survival predictions made with residual convolutional neural network-determined morphological response (ResNet-MR) in patients with unresectable synchronous liver-only metastatic colorectal cancer (mCRC) treated with bev...
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