Nuklearmedizin - NuclearMedicine, Table of Contents Nuklearmedizin 2021; 60(05): 321-324DOI: 10.1055/a-1542-6231 Editorial AI in nuclear medicine – what, why and how?KI in der Nuklearmedizin – Was, warum und wie?Authors Julian Manuel Michael Rogasch Tobias Penzkofer Recommend Article Abstract Buy Article(opens in new window) Full Text References 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
