AIMC Topic: Tomography, X-Ray Computed
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Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification.
Scientific reports
Apr 11, 2025
To address the public health issue of renal failure and the global shortage of nephrologists, an AI-based system has been developed to automatically identify kidney diseases. Recent advancements in machine learning, deep learning (DL), and artificial...
Evaluating the dosimetric and positioning accuracy of a deep learning based synthetic-CT model for liver radiotherapy treatment planning.
Biomedical physics & engineering express
Apr 11, 2025
An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation. This study evaluated the dosimetric and patient positioning accuracy of deep learning-generated sCT for liver radiotherapy.sCT images were generated ...
Incorporating frequency domain features into radiomics for improved prognosis of esophageal cancer.
Medical & biological engineering & computing
Apr 10, 2025
Esophageal cancer is a highly aggressive gastrointestinal malignancy with a poor prognosis, making accurate prognostic assessment essential for patient care. The performance of the esophageal cancer prognosis model based on conventional radiomics is ...
Machine-Learning-Based Computed Tomography Radiomics Regression Model for Predicting Pulmonary Function.
Academic radiology
Apr 10, 2025
RATIONALE AND OBJECTIVES: Chest computed tomography (CT) radiomics can be utilized for categorical predictions; however, models predicting pulmonary function indices directly are lacking. This study aimed to develop machine-learning-based regression ...
Interpretable Machine Learning Radiomics Model Predicts 5-year Recurrence-Free Survival in Non-metastatic Clear Cell Renal Cell Carcinoma: A Multicenter and Retrospective Cohort Study.
Academic radiology
Apr 10, 2025
RATIONALE AND OBJECTIVES: To develop and validate a computed tomography (CT) radiomics-based interpretable machine learning (ML) model for predicting 5-year recurrence-free survival (RFS) in non-metastatic clear cell renal cell carcinoma (ccRCC).
Image quality improvement in head and neck angiography based on dual-energy CT and deep learning.
BMC medical imaging
Apr 10, 2025
OBJECTIVE: Compare the image quality of image reconstructed using deep learning-based image reconstruction (DLIR) and iterative reconstruction algorithms for head and neck dual-energy CT angiography (DECTA).
Neck
Prospective Studies
Head
Middle Aged
Radiographic Image Interpretation, Computer-Assisted
Tomography, X-Ray Computed
Signal-To-Noise Ratio
Aged
Humans
Deep Learning
Quality Improvement
Female
Computed Tomography Angiography
Aged, 80 and over
Algorithms
Head and Neck Neoplasms
Artifacts
Radiography, Dual-Energy Scanned Projection
Adult
Male
Preoperative prediction of pulmonary ground-glass nodule infiltration status by CT-based radiomics combined with neural networks.
BMC cancer
Apr 10, 2025
OBJECTIVE: The infiltration status of pulmonary ground-glass nodules (GGNs) exhibits significant variability, demanding tailored surgical strategies and individualized postoperative adjuvant therapies. This study explored the preoperative assessment ...
Enhancing neurological disease diagnostics: fusion of deep transfer learning with optimization algorithm for acute brain stroke prediction using facial images.
Scientific reports
Apr 10, 2025
Stroke is a main risk to life and fitness in current society, particularly in the aging population. Also, the stroke is recognized as a cerebrovascular accident. It contains a nervous illness, which can result from haemorrhage or ischemia of the brai...
Lung nodule detection using a multi-scale convolutional neural network and global channel spatial attention mechanisms.
Scientific reports
Apr 10, 2025
Early detection of lung nodules is crucial for the prevention and treatment of lung cancer. However, current methods face challenges such as missing small nodules, variations in nodule size, and high false positive rates. To address these challenges,...
Semantic-consistent diffusion model for unsupervised traumatic brain injury detection and segmentation from computed tomography images.
Medical physics
Apr 9, 2025
BACKGROUND: Unsupervised traumatic brain injury (TBI) lesion detection aims to identify and segment abnormal regions, such as cerebral edema and hemorrhages, using only healthy training data. Recent advancements in generative models have achieved suc...
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