AIMC Topic: Tomography, X-Ray Computed
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The role of artificial intelligence in the diagnosis, imaging, and treatment of thoracic empyema.
Current opinion in pulmonary medicine
Dec 23, 2024
PURPOSE OF REVIEW: The management of thoracic empyema is often complicated by diagnostic delays, recurrence, treatment failures and infections with antibiotic resistant bacteria. The emergence of artificial intelligence (AI) in healthcare, particular...
AI-derived automated quantification of cardiac chambers and myocardium from non-contrast CT: Prediction of major adverse cardiovascular events in asymptomatic subjects.
Atherosclerosis
Dec 22, 2024
BACKGROUND AND AIMS: The significance of left ventricular mass and chamber volumes from non-contrast computed tomography (CT) for predicting major adverse cardiovascular events (MACE) has not been studied. Our objective was to evaluate the role of ar...
Asymptomatic Diseases
Risk Assessment
Deep Learning
Female
Radiographic Image Interpretation, Computer-Assisted
Predictive Value of Tests
Coronary Artery Disease
Humans
Coronary Angiography
Aged
Heart Ventricles
Artificial Intelligence
Male
Vascular Calcification
Prognosis
Middle Aged
Tomography, X-Ray Computed
Improving functional correlation of quantification of interstitial lung disease by reducing the vendor difference of CT using generative adversarial network (GAN) style conversion.
European journal of radiology
Dec 22, 2024
OBJECTIVE: To assess whether CT style conversion between different CT vendors using a routable generative adversarial network (RouteGAN) could minimize variation in ILD quantification, resulting in improved functional correlation of quantitative CT (...
Female
Reproducibility of Results
Aged
Male
Radiographic Image Interpretation, Computer-Assisted
Middle Aged
Respiratory Function Tests
Humans
Lung Diseases, Interstitial
Lung
Generative Adversarial Networks
Retrospective Studies
Deep Learning
Tomography, X-Ray Computed
Idiopathic Pulmonary Fibrosis
Image quality assessment of artificial intelligence iterative reconstruction for low dose unenhanced abdomen: comparison with hybrid iterative reconstruction.
Abdominal radiology (New York)
Dec 21, 2024
OBJECTIVES: To assess the impact of artificial intelligence iterative reconstruction algorithms (AIIR) on image quality with phantom and clinical studies.
Deep learning model for automated detection of fresh and old vertebral fractures on thoracolumbar CT.
European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society
Dec 21, 2024
PURPOSE: To develop a deep learning system for automatic segmentation of compression fracture vertebral bodies on thoracolumbar CT and differentiate between fresh and old fractures.
Application of machine learning methods for predicting esophageal variceal bleeding in patients with cirrhosis.
European radiology
Dec 21, 2024
OBJECTIVE: To develop and compare machine learning models based on CT morphology features, serum biomarkers, and basic physical conditions to predict esophageal variceal bleeding.
SMDFnet: Saliency multiscale dense fusion network for MRI and CT image fusion.
Computers in biology and medicine
Dec 21, 2024
MRI-CT image fusion technology combines magnetic resonance imaging (MRI) and computed tomography (CT) imaging to provide more comprehensive and accurate image information. This fusion technology can play an important role in medical diagnosis and sur...
Improving the prediction of chemotherapy dose-limiting toxicity in colon cancer patients using an AI-CT-based 3D body composition of the entire L1-L5 lumbar spine.
Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer
Dec 21, 2024
PURPOSE: Chemotherapy dose-limiting toxicities (DLT) pose a significant challenge in successful colon cancer treatment. Body composition analysis may enable tailored interventions thereby supporting the mitigation of chemotherapy toxic effects. This ...
Identifying threshold of CT-defined muscle loss after radiotherapy for survival in oral cavity cancer using machine learning.
European radiology
Dec 20, 2024
OBJECTIVES: Muscle loss after radiotherapy is associated with poorer survival in patients with oral cavity squamous cell carcinoma (OCSCC). However, the threshold of muscle loss remains unclear. This study aimed to utilize explainable artificial inte...
CT-based clinical-radiomics model to predict progression and drive clinical applicability in locally advanced head and neck cancer.
European radiology
Dec 20, 2024
BACKGROUND: Definitive chemoradiation is the primary treatment for locally advanced head and neck carcinoma (LAHNSCC). Optimising outcome predictions requires validated biomarkers, since TNM8 and HPV could have limitations. Radiomics may enhance risk...
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