AIMC Topic: Tomography, X-Ray Computed

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COVID‑19 detection from chest X-ray images using transfer learning.

Scientific reports May 21, 2024
COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 ...
Radiography, Thoracic Betacoronavirus Pneumonia, Viral Sensitivity and Specificity Deep Learning COVID-19 Coronavirus Infections Tomography, X-Ray Computed SARS-CoV-2 Humans Neural Networks, Computer Pandemics
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Enhancing cancer prediction in challenging screen-detected incident lung nodules using time-series deep learning.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society May 20, 2024
Lung cancer screening (LCS) using annual computed tomography (CT) scanning significantly reduces mortality by detecting cancerous lung nodules at an earlier stage. Deep learning algorithms can improve nodule malignancy risk stratification. However, t...
Early Detection of Cancer Humans Radiographic Image Interpretation, Computer-Assisted Diagnosis, Computer-Assisted Algorithms Deep Learning Tomography, X-Ray Computed Lung Neoplasms
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Computed tomography machine learning classifier correlates with mortality in interstitial lung disease.

Respiratory investigation May 20, 2024
BACKGROUND: A machine learning classifier system, Fibresolve, was designed and validated as an adjunct to non-invasive diagnosis in idiopathic pulmonary fibrosis (IPF). The system uses a deep learning algorithm to analyze chest computed tomography (C...
Humans Machine Learning Tomography, X-Ray Computed Female Aged Male Predictive Value of Tests Lung Diseases, Interstitial Middle Aged Follow-Up Studies Idiopathic Pulmonary Fibrosis
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Deep Learning Method for Rapid Simultaneous Multistructure Temporal Bone Segmentation.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery May 20, 2024
OBJECTIVE: To develop and validate a deep learning algorithm for the automated segmentation of key temporal bone structures from clinical computed tomography (CT) data sets.
Algorithms Tomography, X-Ray Computed Cross-Sectional Studies Deep Learning Humans Temporal Bone
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Synthetic CT generation based on CBCT using improved vision transformer CycleGAN.

Scientific reports May 20, 2024
Cone-beam computed tomography (CBCT) is a crucial component of adaptive radiation therapy; however, it frequently encounters challenges such as artifacts and noise, significantly constraining its clinical utility. While CycleGAN is a widely employed ...
Image Processing, Computer-Assisted Unsupervised Machine Learning Humans Algorithms Neural Networks, Computer Tomography, X-Ray Computed Cone-Beam Computed Tomography
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Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT.

European radiology experimental May 20, 2024
BACKGROUND: Emphysema influences the appearance of lung tissue in computed tomography (CT). We evaluated whether this affects lung nodule detection by artificial intelligence (AI) and human readers (HR).
Solitary Pulmonary Nodule Radiographic Image Interpretation, Computer-Assisted Pulmonary Emphysema Male Middle Aged Lung Neoplasms Radiation Dosage Artificial Intelligence Software Tomography, X-Ray Computed Female Aged Sensitivity and Specificity Humans
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Radiomics and deep learning models for CT pre-operative lymph node staging in pancreatic ductal adenocarcinoma: A systematic review and meta-analysis.

European journal of radiology May 18, 2024
PURPOSE: To evaluate the diagnostic accuracy of computed tomography (CT)-based radiomic algorithms and deep learning models to preoperatively identify lymph node metastasis (LNM) in patients with pancreatic ductal adenocarcinoma (PDAC).
Neoplasm Staging Carcinoma, Pancreatic Ductal Humans Tomography, X-Ray Computed Lymph Nodes Preoperative Care Radiomics Lymphatic Metastasis Deep Learning Pancreatic Neoplasms
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Machine learning and radiomics analysis by computed tomography in colorectal liver metastases patients for RAS mutational status prediction.

La Radiologia medica May 18, 2024
PURPOSE: To assess the efficacy of machine learning and radiomics analysis by computed tomography (CT) in presurgical setting, to predict RAS mutational status in colorectal liver metastases.
Retrospective Studies Tomography, X-Ray Computed Female Reproducibility of Results Humans Machine Learning Male Middle Aged Liver Neoplasms Predictive Value of Tests Mutation Sensitivity and Specificity Aged Colorectal Neoplasms Aged, 80 and over Radiomics Adult
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Points of interest linear attention network for real-time non-rigid liver volume to surface registration.

Medical physics May 17, 2024
BACKGROUND: In laparoscopic liver surgery, accurately predicting the displacement of key intrahepatic anatomical structures is crucial for informing the doctor's intraoperative decision-making. However, due to the constrained surgical perspective, on...
Imaging, Three-Dimensional Image Processing, Computer-Assisted Liver Neural Networks, Computer Humans Organ Size Tomography, X-Ray Computed Time Factors
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Deep learning reconstruction computed tomography with low-dose imaging.

Pediatric radiology May 17, 2024
Deep Learning Humans Radiographic Image Interpretation, Computer-Assisted Child Tomography, X-Ray Computed Radiation Dosage
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