Digital Twin Technology In Radiology.
Authors
Affiliations (9)
Affiliations (9)
- Decision Neuroscience Laboratory, Center for Brain, Biology, and Behavior, University of Nebraska, Lincoln, NE, 68503, USA. [email protected].
- Cancer Digital Twin, San Mateo, CA, USA.
- Multiple Sclerosis Research Center, Neuroscience Institute, Tehran University of Medical Sciences, Tehran, Iran.
- Mayo Clinic Artificial Intelligence Laboratory, Department of Radiology, Mayo Clinic, Rochester, MN, USA.
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA.
- Department of Diagnostic Imaging, Universidade Federal de São Paulo, Rua Napoleão de Barros 800, São Paulo, SP, 04024-000, Brazil.
- Bunkerhill Health, San Francisco, CA, USA.
- Department of Radiology, New York University Grossman School of Medicine, New York, USA.
- Department of Radiology, Mayo Clinic, Rochester, MN, USA. [email protected].
Abstract
A digital twin is a computational model that provides a virtual representation of a specific physical object, system, or process and predicts its behavior at future time points. These simulation models form computational profiles for new diagnosis and prevention models. The digital twin is a concept borrowed from engineering. However, the rapid evolution of this technology has extended its application across various industries. In recent years, digital twins in healthcare have gained significant traction due to their potential to revolutionize medicine and drug development. In the context of radiology, digital twin technology can be applied in various areas, including optimizing medical device design, improving system performance, facilitating personalized medicine, conducting virtual clinical trials, and educating radiology trainees. Also, radiologic image data is a critical source of patient-specific measures that play a role in generating advanced intelligent digital twins. Generating a practical digital twin faces several challenges, including data availability, computational techniques, validation frameworks, and uncertainty quantification, all of which require collaboration among engineers, healthcare providers, and stakeholders. This review focuses on recent trends in digital twin technology and its intersection with radiology by reviewing applications, technological advancements, and challenges that need to be addressed for successful implementation in the field.