Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations. Issue 3 (May 2020)

• Record Type: Journal Article
• Authors: Fischer, Andreas M.; Yacoub, Basel; Savage, Rock H.; Martinez, John D.; Wichmann, Julian L.; Sahbaee, Pooyan; Grbic, Sasa; Varga-Szemes, Akos; Schoepf, U. Joseph
• Is Part Of: Journal of thoracic imaging. Volume 35:Issue 3(2020)Supplement 1
• Publication Date: 2020-05
• Subjects: machine learning -- artificial intelligence -- convolutional neural network -- chest computed tomography -- pulmonary emphysema -- pulmonary nodules -- aortic enlargment -- bone mineral density; Chest -- Radiography -- Periodicals; Chest -- Diseases -- Diagnosis -- Periodicals; 617.540757
• DOI: 10.1097/RTI.0000000000000498
• Languages: English
• ISSNs: 0883-5993

Abstract

Abstract : 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.