AIMC Topic: Tomography, X-Ray Computed
Clear Filters Showing 501 to 510 of 4963 articles
Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis.
BMC medical imaging
Jan 17, 2025
OBJECTIVE: In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially when 3D consolidation-to-tumor ratio (CTR) ≥ 50%, as malignant ones are more invasive. This study aim...
Association between automatic AI-based quantification of airway-occlusive mucus plugs and all-cause mortality in patients with COPD.
Thorax
Jan 17, 2025
In this cohort study involving 9399 current and former smokers from the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease study, we assessed the relationship between artificial intelligence-quantified mucus plugs on chest CTs and all-caus...
New imaging techniques and trends in radiology.
Diagnostic and interventional radiology (Ankara, Turkey)
Jan 16, 2025
Radiography is a field of medicine inherently intertwined with technology. The dependency on technology is very high for obtaining images in ultrasound (US), computed tomography (CT), and magnetic resonance imaging (MRI). Although the reduction in ra...
Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model.
Diagnostic and interventional radiology (Ankara, Turkey)
Jan 16, 2025
PURPOSE: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomarkers to accurately predict its therapeutic efficacy. The present study aimed to construct individual...
Evaluation of a Deep Learning Denoising Algorithm for Dose Reduction in Whole-Body Photon-Counting CT Imaging: A Cadaveric Study.
Academic radiology
Jan 15, 2025
RATIONALE AND OBJECTIVES: Photon Counting CT (PCCT) offers advanced imaging capabilities with potential for substantial radiation dose reduction; however, achieving this without compromising image quality remains a challenge due to increased noise at...
A novel hybrid deep learning framework based on biplanar X-ray radiography images for bone density prediction and classification.
Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
Jan 15, 2025
UNLABELLED: This study utilized deep learning for bone mineral density (BMD) prediction and classification using biplanar X-ray radiography (BPX) images from Huashan Hospital Medical Checkup Center. Results showed high accuracy and strong correlation...
Development of an interpretable machine learning model based on CT radiomics for the prediction of post acute pancreatitis diabetes mellitus.
Scientific reports
Jan 15, 2025
This study sought to establish and validate an interpretable CT radiomics-based machine learning model capable of predicting post-acute pancreatitis diabetes mellitus (PPDM-A), providing clinicians with an effective predictive tool to aid patient man...
Extracting organs of interest from medical images based on convolutional neural network with auxiliary and refined constraints.
Scientific reports
Jan 15, 2025
Accurately extracting organs from medical images provides radiologist with more comprehensive evidences to clinical diagnose, which offers up a higher accuracy and efficiency. However, the key to achieving accurate segmentation lies in abundant clues...
Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study.
European radiology experimental
Jan 15, 2025
BACKGROUND: Hemorrhagic transformation (HT) is a complication of reperfusion therapy following acute ischemic stroke (AIS). We aimed to develop and validate a model for predicting HT and its subtypes with poor prognosis-parenchymal hemorrhage (PH), i...
An efficient deep unrolling network for sparse-view CT reconstruction via alternating optimization of dense-view sinograms and images.
Physics in medicine and biology
Jan 15, 2025
. Recently, there have been many advancements in deep unrolling methods for sparse-view computed tomography (SVCT) reconstruction. These methods combine model-based and deep learning-based reconstruction techniques, improving the interpretability and...
Don't Miss the Future of Medicine
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.
Subscribe NowPopular Topics
- Machine Learning 32544
- Neural Networks, Computer 30502
- Algorithms 27746
- Deep Learning 27302
- Artificial Intelligence 25227
- Robotics 13430
- Image Processing, Computer-Assisted 9890
- Retrospective Studies 9537
- Magnetic Resonance Imaging 6484
- Reproducibility of Results 5746
- Tomography, X-Ray Computed 4963
- Support Vector Machine 4812
- Computational Biology 4397
- Brain 4188
- Computer Simulation 3963
Recent Journals
- Scientific reports 7013
- Sensors (Basel, Switzerland) 4794
- PloS one 4326
- Neural networks : the official journal of the International Neural Network Society 3035
- Computers in biology and medicine 2040
- Computational intelligence and neuroscience 1931
- Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 1686
- Studies in health technology and informatics 1395
- IEEE journal of biomedical and health informatics 1197
- Journal of chemical information and modeling 1088
- Nature communications 1072
- Computer methods and programs in biomedicine 895
- Journal of medical Internet research 880
- Bioinformatics (Oxford, England) 880
- IEEE transactions on neural networks and learning systems 859