Seminars in nuclear medicine
-
Artificial intelligence and machine learning based approaches are increasingly finding their way into various areas of nuclear medicine imaging. With the technical development of new methods and the expansion to new fields of application, this trend is likely to become even more pronounced in future. Possible means of application range from automated image reading and classification to correlation with clinical outcomes and to technological applications in image processing and reconstruction. ⋯ Most artificial intelligence approaches in nuclear medicine imaging are still in early stages of development, further improvements are necessary for broad clinical applications. In this review, we describe the current trends in the context fields of body oncology, cardiac imaging, and neuroimaging while an additional section puts emphasis on technological trends. Our aim is not only to describe currently available methods, but also to place a special focus on the description of possible future developments.
-
The emergence of artificial intelligence (AI) in nuclear medicine has occurred over the last 50 years but more recent developments in machine learning (ML) and deep learning (DL) have driven new capabilities of AI in nuclear medicine. In nuclear medicine, the artificial neural network (ANN) is the backbone of ML and DL. The inputs may be radiomic features that have been extracted from the image files or, if using a convolutional neural network (CNN), may be the images themselves. ⋯ There are also applications of ANNs and ML that allow small datasets (and larger ones) to be analysed in parallel to conventional statistical analysis. AI has assimilated into the clinical and research practice of nuclear medicine with little disruption. The emergence of ML and DL applications, however, has produced a seismic significant shift in the clinical and research landscape that demands at least rudimentary understanding of the principles of AI, ANNs and CNNs among nuclear medicine professionals.