Intelligent Imaging in Nuclear Medicine: the Principles of Artificial Intelligence, Machine Learning and Deep Learning

Abstract

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. AI in nuclear medicine re-engineers and re-imagines clinical and research capabilities. An understanding of the principles of AI, ML and DL contextualised to nuclear medicine allows richer engagement in clinical and research applications, and capacity for problem solving where required. Simple applications of ML include quality assurance, risk assessment, business analytics and rudimentary classifications. More complex applications of DL for detection, localisation, classification, segmentation, quantitation and radiomic feature extraction using CNNs can be applied to general nuclear medicine, SPECT, PET, CT and MRI. 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.