Synthetic data in radiological imaging: current state and future outlook

Author:

Sizikova Elena1ORCID,Badal Andreu1,Delfino Jana G1,Lago Miguel1,Nelson Brandon1,Saharkhiz Niloufar1,Sahiner Berkman1,Zamzmi Ghada1,Badano Aldo1

Affiliation:

1. Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, U.S. Food and Drug Administration , Silver Spring, MD 20993, United States

Abstract

Abstract A key challenge for the development and deployment of artificial intelligence (AI) solutions in radiology is solving the associated data limitations. Obtaining sufficient and representative patient datasets with appropriate annotations may be burdensome due to high acquisition cost, safety limitations, patient privacy restrictions, or low disease prevalence rates. In silico data offers a number of potential advantages to patient data, such as diminished patient harm, reduced cost, simplified data acquisition, scalability, improved quality assurance testing, and a mitigation approach to data imbalances. We summarize key research trends and practical uses for synthetically generated data for radiological applications of AI. Specifically, we discuss different types of techniques for generating synthetic examples, their main application areas, and related quality control assessment issues. We also discuss current approaches for evaluating synthetic imaging data. Overall, synthetic data holds great promise in addressing current data availability gaps, but additional work is needed before its full potential is realized.

Funder

Department of Health and Human Services

Publisher

Oxford University Press (OUP)

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