AIMC Topic: Tomography, X-Ray Computed
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Preoperative clinical radiomics model based on deep learning in prognostic assessment of patients with gallbladder carcinoma.
BMC cancer
Feb 25, 2025
OBJECTIVE: We aimed to develop a preoperative clinical radiomics survival prediction model based on the radiomics features via deep learning to provide a reference basis for preoperative assessment and treatment decisions for patients with gallbladde...
Detecting severe coronary artery stenosis in T2DM patients with NAFLD using cardiac fat radiomics-based machine learning.
Scientific reports
Feb 25, 2025
To analyze radiomics features of cardiac adipose tissue in individuals with type 2 diabetes (T2DM) and non-alcoholic fatty liver disease (NAFLD), integrating relevant clinical indicators, and employing machine learning techniques to construct a preci...
Development and validation of a deep reinforcement learning algorithm for auto-delineation of organs at risk in cervical cancer radiotherapy.
Scientific reports
Feb 25, 2025
This study was conducted to develop and validate a novel deep reinforcement learning (DRL) algorithm incorporating the segment anything model (SAM) to enhance the accuracy of automatic contouring organs at risk during radiotherapy for cervical cancer...
Deep Learning-Enhanced Ultra-high-resolution CT Imaging for Superior Temporal Bone Visualization.
Academic radiology
Feb 24, 2025
RATIONALE AND OBJECTIVES: This study assesses the image quality of temporal bone ultra-high-resolution (UHR) Computed tomography (CT) scans in adults and children using hybrid iterative reconstruction (HIR) and a novel, vendor-specific deep learning-...
Artificial Intelligence in CT for Predicting Cervical Lymph Node Metastasis in Papillary Thyroid Cancer Patients: A Meta-analysis.
Academic radiology
Feb 24, 2025
PURPOSE: This meta-analysis aims to evaluate the diagnostic performance of CT-based artificial intelligence (AI) in diagnosing cervical lymph node metastasis (LNM) of papillary thyroid cancer (PTC).
AI for image quality and patient safety in CT and MRI.
European radiology experimental
Feb 23, 2025
Substantial endeavors have been recently dedicated to developing artificial intelligence (AI) solutions, especially deep learning-based, tailored to enhance radiological procedures, in particular algorithms designed to minimize radiation exposure and...
Multi-dimensional consistency learning between 2D Swin U-Net and 3D U-Net for intestine segmentation from CT volume.
International journal of computer assisted radiology and surgery
Feb 22, 2025
PURPOSE: The paper introduces a novel two-step network based on semi-supervised learning for intestine segmentation from CT volumes. The intestine folds in the abdomen with complex spatial structures and contact with neighboring organs that bring dif...
A hybrid inception-dilated-ResNet architecture for deep learning-based prediction of COVID-19 severity.
Scientific reports
Feb 22, 2025
Chest computed tomography (CT) scans are essential for accurately assessing the severity of the novel Coronavirus (COVID-19), facilitating appropriate therapeutic interventions and monitoring disease progression. However, determining COVID-19 severit...
Measuring kidney stone volume - practical considerations and current evidence from the EAU endourology section.
Current opinion in urology
Feb 21, 2025
PURPOSE OF REVIEW: This narrative review provides an overview of the use, differences, and clinical impact of current methods for kidney stone volume assessment.
Incorporating indirect MRI information in a CT-based deep learning model for prostate auto-segmentation.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Feb 21, 2025
BACKGROUND AND PURPOSE: Computed tomography (CT) imaging poses challenges for delineation of soft tissue structures for prostate cancer external beam radiotherapy. Guidelines require the input of magnetic resonance imaging (MRI) information. We devel...
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