Work

Addressing the Generalizability of AI in Radiology Using a Novel Data Augmentation Framework with Synthetic Patient Image Data: Proof-of-Concept and External Validation for Classification Tasks in Multiple Sclerosis

Year: 2024

Type: article

Abstract: Generative adversarial network–based synthetic data augmentation during model training substantially improved performance of artificial intelligence models when applied to external MRI data and may be extended to a variety of clinical tasks to reduce domain shift on unseen data.

Source: Radiology Artificial Intelligence

DOI: 10.1148/ryai.230514

Authors: Gianluca Brugnara, Chandrakanth Jayachandran Preetha, Katerina Deike, Robert Haase, Thomas Pinetz, Martha Foltyn-Dumitru, Mustafa A. Mahmutoglu, Brigitte Wildemann, Ricarda Diem, Wolfgang Wick, Alexander Radbruch, Martin Bendszus, Hagen Meredig, Aditya Rastogi, Philipp Vollmuth

Institutions: German Cancer Research Center, University Hospital Bonn, University Hospital Heidelberg, University of Bonn

Language: English

Topic: Artificial Intelligence in Healthcare and Education

Subfield: Health Informatics

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institutes of Health, National Institute on Aging, National Institute of Biomedical Imaging and Bioengineering, National Center for Advancing Translational Sciences

Awards: G1050368716, G1423396920, G1602390028, G2202404319, G4427631003, G587293260, G9000005743, G9000019529, G9000022006, G9000060740, G9000096510, G9000112288, G9000115080, G9000217785, G9000266300, Alzheimer's Disease Research Center

OpenAlex ID: W4403459459

FWCI: 1.353

Cites: 20

Cited by: 17

Related to: 10