AIMC Topic: Tomography, X-Ray Computed
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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...
Generative Adversarial Networks With Radiomics Supervision for Lung Lesion Generation.
IEEE transactions on bio-medical engineering
Jan 15, 2025
Data-driven methods for lesion generation are quickly emerging due to the need for realistic imaging targets for image quality assessment and virtual clinical trials. We proposed a generative adversarial network (GAN) architecture for conditional gen...
A deep learning approach versus expert clinician panel in the classification of posterior circulation infarction.
NeuroImage. Clinical
Jan 12, 2025
BACKGROUND: Posterior circulation infarction (POCI) is common. Imaging techniques such as non-contrast-CT (NCCT) and diffusion-weighted-magnetic-resonance-imaging commonly fail to detect hyperacute POCI. Studies suggest expert inspection of Computed ...
A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort.
Scientific reports
Jan 11, 2025
Deep learning (DL) methods have demonstrated remarkable effectiveness in assisting with lung cancer risk prediction tasks using computed tomography (CT) scans. However, the lack of comprehensive comparison and validation of state-of-the-art (SOTA) mo...
Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V.
European radiology
Jan 10, 2025
OBJECTIVES: To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain comp...
Artificial intelligence for left ventricular hypertrophy detection and differentiation on echocardiography, cardiac magnetic resonance and cardiac computed tomography: A systematic review.
International journal of cardiology
Jan 10, 2025
AIMS: Left ventricular hypertrophy (LVH) is a common clinical finding associated with adverse cardiovascular outcomes. Once LVH is diagnosed, defining its cause has crucial clinical implications. Artificial intelligence (AI) may allow significant pro...
AI based medical imagery diagnosis for COVID-19 disease examination and remedy.
Scientific reports
Jan 10, 2025
COVID-19, caused by the SARS-CoV-2 coronavirus, has spread to more than 200 countries, affecting millions, costing billions, and claiming nearly 2 million lives since late 2019. This highly contagious disease can easily overwhelm healthcare systems i...
Deep learning algorithms enable MRI-based scapular morphology analysis with values comparable to CT-based assessments.
Scientific reports
Jan 10, 2025
Scapular morphological attributes show promise as prognostic indicators of retear following rotator cuff repair. Current evaluation techniques using single-slice magnetic-resonance imaging (MRI) are, however, prone to error, while more accurate compu...
Interpretable CT Radiomics-based Machine Learning Model for Preoperative Prediction of Ki-67 Expression in Clear Cell Renal Cell Carcinoma.
Academic radiology
Jan 9, 2025
RATIONALE AND OBJECTIVES: To develop and externally validate interpretable CT radiomics-based machine learning (ML) models for preoperative Ki-67 expression prediction in clear cell renal cell carcinoma (ccRCC).
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