Clinical issue: Hybrid imaging enables the precise visualization of cellular metabolism by combining anatomical and metabolic information. Advances in artificial intelligence (AI) offer new methods for processing and evaluating this data.
Methodological innovations: This review summarizes current developments and applications of AI methods in hybrid imaging. Applications in image processing as well as methods for disease-related evaluation are presented and discussed.
Materials and methods: This article is based on a selective literature search with the search engines PubMed and arXiv.
Assessment: Currently, there are only a few AI applications using hybrid imaging data and no applications are established in clinical routine yet. Although the first promising approaches are emerging, they still need to be evaluated prospectively. In the future, AI applications will support radiologists and nuclear medicine radiologists in diagnosis and therapy.
Keywords: Cellular metabolism; Deep learning; Deep neuronal networks; Diagnostic imaging; Machine learning.