AI in nuclear medicine – what, why and how?

What is artificial intelligence (AI)?

There have been various attempts to define artificial intelligence (AI), and none is sufficiently precise but at the same time universally applicable. However, in the context of medical imaging, the term machine learning (ML), which is generally considered a subset of AI [1], may describe most applications more appropriately. Here, “learning” relates to the capability of systems to identify complex relationships between data and to predict outcomes in new and unknown data with similar characteristics. With the computing power available today, ML has advanced from classical ML methods, such as decision trees or support vector machines, to more complex architectures, such as deep learning. This uses “deep” artificial neural networks, which are characterized by multiple layers of artificial neurons [2]. In several applications in medical imaging, deep learning has been found to be equivalent or superior to classical ML methods [3] [4] [5], and it is now the most commonly used ML approach for such tasks. Deep neural networks, and especially convolutional neural networks (yet another subset), are inherently useful for the numerous “visual tasks” involved in image analysis.

Publication History

Received: 25 May 2021

Accepted: 01 July 2021

Article published online:
04 October 2021

• References

• 1 Sindhu V. AN EMPIRICAL SCIENCE RESEARCH ON BIOINFORMATICS IN MACHINE LEARNING. JOURNAL OF MECHANICS OF CONTINUA AND MATHEMATICAL SCIENCES 2020; spl7.
• 2 Schmidhuber J. Deep learning in neural networks: An overview. Neural Networks 2015; 61: 85-117
• 3 Han Y, Ma Y, Wu Z. et al. Histologic subtype classification of non-small cell lung cancer using PET/CT images. European journal of nuclear medicine and molecular imaging 2021; 48: 350-360
• 4 Hartenstein A, Lübbe F, Baur ADJ. et al. Prostate Cancer Nodal Staging: Using Deep Learning to Predict 68Ga-PSMA-Positivity from CT Imaging Alone. Scientific Reports 2020; 10: 3398
• 5 Wang H, Zhou Z, Li Y. et al. Comparison of machine learning methods for classifying mediastinal lymph node metastasis of non-small cell lung cancer from 18F-FDG PET/CT images. EJNMMI Research 2017; 7: 11
• 6 Saltybaeva N, Schmidt B, Wimmer A. et al. Precise and Automatic Patient Positioning in Computed Tomography: Avatar Modeling of the Patient Surface Using a 3-Dimensional Camera. Invest Radiol 2018; 53: 641-646
• 7 Yang J, Sohn JH, Behr SC. et al. CT-less Direct Correction of Attenuation and Scatter in the Image Space Using Deep Learning for Whole-Body FDG PET: Potential Benefits and Pitfalls. Radiology: Artificial Intelligence 2020; 3: e200137
• 8 Reader A, Corda-D'Incan G, Mehranian A. et al. Deep Learning for PET Image Reconstruction. IEEE Transactions on Radiation and Plasma Medical Sciences 2020; 1-1
• 9 Zhao Y, Gafita A, Vollnberg B. et al. Deep neural network for automatic characterization of lesions on (68)Ga-PSMA-11 PET/CT. European journal of nuclear medicine and molecular imaging 2020; 47: 603-613
• 10 Sibille L, Seifert R, Avramovic N. et al. (18)F-FDG PET/CT Uptake Classification in Lymphoma and Lung Cancer by Using Deep Convolutional Neural Networks. Radiology 2020; 294: 445-452
• 11 Schwyzer M, Martini K, Benz DC. et al. Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance. European radiology 2020; 30: 2031-2040
• 12 Baek S, He Y, Allen BG. et al. Deep segmentation networks predict survival of non-small cell lung cancer. Scientific Reports 2019; 9: 17286
• 13 Peng H, Dong D, Fang M-J. et al. Prognostic Value of Deep Learning PET/CT-Based Radiomics: Potential Role for Future Individual Induction Chemotherapy in Advanced Nasopharyngeal Carcinoma. Clinical Cancer Research 2019; 25: 4271-4279
• 14 Betancur J, Otaki Y, Motwani M. et al. Prognostic Value of Combined Clinical and Myocardial Perfusion Imaging Data Using Machine Learning. JACC: Cardiovascular Imaging 2018; 11: 1000-1009
• 15 Hu L-H, Betancur J, Sharir T. et al. Machine learning predicts per-vessel early coronary revascularization after fast myocardial perfusion SPECT: results from multicentre REFINE SPECT registry. European Heart Journal - Cardiovascular Imaging 2019; 21: 549-559
• 16 Ding Y, Sohn JH, Kawczynski MG. et al. A Deep Learning Model to Predict a Diagnosis of Alzheimer Disease by Using (18)F-FDG PET of the Brain. Radiology 2019; 290: 456-464
• 17 Bukowski M, Farkas R, Beyan O. et al. Implementation of eHealth and AI integrated diagnostics with multidisciplinary digitized data: are we ready from an international perspective?. European radiology 2020; 30: 5510-5524
• 18 Sutton RT, Pincock D, Baumgart DC. et al. An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med 2020; 3: 17
• 19 Koitka S, Kim MS, Qu M. et al. Mimicking the radiologists' workflow: Estimating pediatric hand bone age with stacked deep neural networks. Medical image analysis 2020; 64: 101743
• 20 Masood A, Yang P, Sheng B. et al. Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT. IEEE journal of translational engineering in health and medicine 2020; 8: 4300113
• 21 Semler SC, Wissing F, Heyder R. German Medical Informatics Initiative. Methods of information in medicine 2018; 57: e50-e56
• 22 Lehne M, Sass J, Essenwanger A. et al. Why digital medicine depends on interoperability. npj Digital Medicine 2019; 2: 79
• 23 Wilkinson MD, Dumontier M, Aalbersberg IJ. et al. The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data 2016; 3: 160018