AIMC Topic: Tomography, X-Ray Computed

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Fully Automated Segmentation of Connective Tissue Compartments for CT-Based Body Composition Analysis: A Deep Learning Approach.

Investigative radiology
OBJECTIVE: Body composition comprises prognostic information in patients with various malignancies and can be opportunistically determined from routine computed tomography (CT) scans. However, accurate assessment of patients with alterations, for exa...

Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review.

Journal of digital imaging
This paper presents a systematic review of the literature focused on the lung nodule detection in chest computed tomography (CT) images. Manual detection of lung nodules by the radiologist is a sequential and time-consuming process. The detection is ...

An Approach to Robotic Testing of the Wrist Using Three-Dimensional Imaging and a Hybrid Testing Methodology.

Journal of biomechanical engineering
Robotic technology is increasingly used for sophisticated in vitro testing designed to understand the subtleties of joint biomechanics. Typically, the joint coordinate systems in these studies are established via palpation and digitization of anatomi...

Image-guidance, Robotics, and the Future of Spine Surgery.

Clinical spine surgery
Spine surgery has seen considerable advancements over the last 2 decades, particularly in the fields of image-guidance and robotics. These technologies offer the potential to overcome the various technical challenges in spinal surgery, such as physic...

Clinical Implementation of Deep Learning in Thoracic Radiology: Potential Applications and Challenges.

Korean journal of radiology
Chest X-ray radiography and computed tomography, the two mainstay modalities in thoracic radiology, are under active investigation with deep learning technology, which has shown promising performance in various tasks, including detection, classificat...

Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations.

Journal of thoracic imaging
The constantly increasing number of computed tomography (CT) examinations poses major challenges for radiologists. In this article, the additional benefits and potential of an artificial intelligence (AI) analysis platform for chest CT examinations i...

Machine Learning and Deep Neural Network Applications in the Thorax: Pulmonary Embolism, Chronic Thromboembolic Pulmonary Hypertension, Aorta, and Chronic Obstructive Pulmonary Disease.

Journal of thoracic imaging
The radiologic community is rapidly integrating a revolution that has not fully entered daily practice. It necessitates a close collaboration between computer scientists and radiologists to move from concepts to practical applications. This article r...

Artificial Intelligence in Diagnostic Imaging: Status Quo, Challenges, and Future Opportunities.

Journal of thoracic imaging
In this review article, the current and future impact of artificial intelligence (AI) technologies on diagnostic imaging is discussed, with a focus on cardio-thoracic applications. The processing of imaging data is described at 4 levels of increasing...

Utilizing Artificial Intelligence to Determine Bone Mineral Density Via Chest Computed Tomography.

Journal of thoracic imaging
PURPOSE: The purpose of this study was to validate the accuracy of an artificial intelligence (AI) prototype application in determining bone mineral density (BMD) from chest computed tomography (CT), as compared with dual-energy x-ray absorptiometry ...