School of Dentistry & Health Sciences, Charles Sturt University, Wagga Wagga, Australia.
Baylor College of Medicine, Houston, TX.
Semin Nucl Med. 2021 Mar;51(2):102-111. doi: 10.1053/j.semnuclmed.2020.08.002. Epub 2020 Sep 11.
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.
人工智能（AI）在核医学中的出现已经有 50 多年了，但机器学习（ML）和深度学习（DL）的最新发展推动了核医学中 AI 的新能力。在核医学中，人工神经网络（ANN）是 ML 和 DL 的核心。输入可以是从图像文件中提取的放射组学特征，或者如果使用卷积神经网络（CNN），则可以是图像本身。核医学中的 AI 重新设计和重新构想了临床和研究能力。对 AI、ML 和 DL 原则的理解，结合核医学的背景，可以更深入地参与临床和研究应用，并在需要时解决问题。ML 的简单应用包括质量保证、风险评估、业务分析和基本分类。更复杂的 DL 应用，如使用 CNN 进行检测、定位、分类、分割、定量和放射组学特征提取，可应用于一般核医学、SPECT、PET、CT 和 MRI。还有一些 ANN 和 ML 的应用可以允许对小数据集（和更大的数据集）进行平行分析，与传统的统计分析相结合。AI 已经融入了核医学的临床和研究实践中，几乎没有产生任何干扰。然而，ML 和 DL 应用的出现，已经在临床和研究领域产生了重大转变，这至少要求核医学专业人员对 AI、ANN 和 CNN 的原理有基本的理解。
