4.3 Review

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

Journal

JOURNAL OF THORACIC IMAGING
Volume 35, Issue -, Pages S21-S27

Publisher

LIPPINCOTT WILLIAMS & WILKINS
DOI: 10.1097/RTI.0000000000000498

Keywords

machine learning; artificial intelligence; convolutional neural network; chest computed tomography; pulmonary emphysema; pulmonary nodules; aortic enlargment; bone mineral density

Ask authors/readers for more resources

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 in routine clinical practice will be examined. Specific application examples include AI-based, fully automatic lung segmentation with emphysema quantification, aortic measurements, detection of pulmonary nodules, and bone mineral density measurement. This contribution aims to appraise this AI-based application for value-added diagnosis during routine chest CT examinations and explore future development perspectives.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.3
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available