AIMC Topic: Tomography, X-Ray Computed

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Current and Potential Applications of Artificial Intelligence in Gastrointestinal Stromal Tumor Imaging.

Contrast media & molecular imaging
The most common mesenchymal tumors are gastrointestinal stromal tumors (GISTs), which have malignant potential and can occur anywhere along the gastrointestinal system. Imaging methods are important and indispensable of GISTs in diagnosis, risk stagi...

Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection.

Biomedical engineering online
BACKGROUND: The large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) meth...

Artificial intelligence to predict the BRAFV600E mutation in patients with thyroid cancer.

PloS one
PURPOSE: To investigate whether a computer-aided diagnosis (CAD) program developed using the deep learning convolutional neural network (CNN) on neck US images can predict the BRAFV600E mutation in thyroid cancer.

Internet of Medical Things: An Effective and Fully Automatic IoT Approach Using Deep Learning and Fine-Tuning to Lung CT Segmentation.

Sensors (Basel, Switzerland)
Several pathologies have a direct impact on society, causing public health problems. Pulmonary diseases such as Chronic obstructive pulmonary disease (COPD) are already the third leading cause of death in the world, leaving tuberculosis at ninth with...

A Deep Learning Pipeline to Automate High-Resolution Arterial Segmentation With or Without Intravenous Contrast.

Annals of surgery
BACKGROUND: Existing methods to reconstruct vascular structures from a computerized tomography (CT) angiogram rely on contrast injection to enhance the radio-density within the vessel lumen. However, pathological changes in the vasculature may be pre...

Natural history of pathologically confirmed pulmonary subsolid nodules with deep learning-assisted nodule segmentation.

European radiology
OBJECTIVE: To explore the natural history of pulmonary subsolid nodules (SSNs) with different pathological types by deep learning-assisted nodule segmentation.

Computer-aided diagnosis of pectus excavatum using CT images and deep learning methods.

Scientific reports
Pectus excavatum (PE) is one of the most common chest wall defects. Accurate assessment of PE deformities is critical for effective surgical intervention. Index-based evaluations have become the standard for objectively estimating PE, however, these ...