Artificial Intelligence in Diagnostic Imaging: Status Quo, Challenges, and Future Opportunities. Issue 3 (May 2020)

• Record Type: Journal Article
• Authors: Sharma, Puneet; Suehling, Michael; Flohr, Thomas; Comaniciu, Dorin
• Is Part Of: Journal of thoracic imaging. Volume 35:Issue 3(2020)Supplement 1
• Publication Date: 2020-05
• Subjects: artificial intelligence -- deep learning -- digital twin; Chest -- Radiography -- Periodicals; Chest -- Diseases -- Diagnosis -- Periodicals; 617.540757
• DOI: 10.1097/RTI.0000000000000499
• Languages: English
• ISSNs: 0883-5993

Abstract

Abstract : 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 complexity and wider implications. At the examination level, AI aims at improving, simplifying, and standardizing image acquisition and processing. Systems for AI-driven automatic patient iso-centering before a computed tomography (CT) scan, patient-specific adaptation of image acquisition parameters, and creation of optimized and standardized visualizations, for example, automatic rib-unfolding, are discussed. At the reading and reporting levels, AI focuses on automatic detection and characterization of features and on automatic measurements in the images. A recently introduced AI system for chest CT imaging is presented that reports specific findings such as nodules, low-attenuation parenchyma, and coronary calcifications, including automatic measurements of, for example, aortic diameters. At the prediction and prescription levels, AI focuses on risk prediction and stratification, as opposed to merely detecting, measuring, and quantifying images. An AI-based approach for individualizing radiation dose in lung stereotactic body radiotherapy is discussed. The digital twin is presented as a concept of individualized computational modeling of human physiology, with AI-based CT-fractional flow reserve modeling as a first example. Finally, at the cohort and population analysis levels, the focus of AI shifts from clinical decision-making to operational decisions.