Upstream Machine Learning in Radiology.
| Autor: | Sandino CM; Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA., Cole EK; Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA., Alkan C; Department of Electrical Engineering, Stanford University, 350 Serra Mall, Stanford, CA 94305, USA., Chaudhari AS; Department of Biomedical Data Science, 1201 Welch Road, Stanford, CA 94305, USA; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA., Loening AM; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA., Hyun D; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA., Dahl J; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA., Imran AA; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA., Wang AS; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA., Vasanawala SS; Department of Radiology, Stanford University, 1201 Welch Road, Stanford, CA 94305, USA. Electronic address: admin_vasanawala@stanford.edu. |
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| Jazyk: | angličtina |
| Zdroj: | Radiologic clinics of North America [Radiol Clin North Am] 2021 Nov; Vol. 59 (6), pp. 967-985. |
| DOI: | 10.1016/j.rcl.2021.07.009 |
| Abstrakt: | Machine learning (ML) and Artificial intelligence (AI) has the potential to dramatically improve radiology practice at multiple stages of the imaging pipeline. Most of the attention has been garnered by applications focused on improving the end of the pipeline: image interpretation. However, this article reviews how AI/ML can be applied to improve upstream components of the imaging pipeline, including exam modality selection, hardware design, exam protocol selection, data acquisition, image reconstruction, and image processing. A breadth of applications and their potential for impact is shown across multiple imaging modalities, including ultrasound, computed tomography, and MRI. (Copyright © 2021 Elsevier Inc. All rights reserved.) |
| Databáze: | MEDLINE |
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