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Literature DB >> 31673788

Artificial intelligence and radiomics in nuclear medicine: potentials and challenges.

Abstract

Artificial intelligence involves a wide range of smart techniques that are applicable to medical services including nuclear medicine. Recent advances in computer power, availability of accumulated digital archives containing large amount of patient images, and records bring new opportunities for the implementation of artificial techniques in nuclear medicine. As a subset of artificial intelligence, machine learning is an emerging tool that possibly perform many clinical tasks. Nuclear medicine community needs to adapt to this fast approaching smart era, to exploit the opportunities and tackle the problems associated with artificial intelligence tools. It is aimed in this editorial to outline the potentials and challenges of artificial intelligence applications in nuclear medicine.

Entities: Species

Keywords: Artificial intelligence; Artificial neural networks; Deep learning; Machine learning; Radiomics; Supervised learning; Unsupervised learning

Mesh：

Year: 2019 PMID： 31673788 DOI： 10.1007/s00259-019-04593-0

Source DB: PubMed Journal: Eur J Nucl Med Mol Imaging ISSN： 1619-7070 Impact factor: 9.236

23 in total

Review 1. Machine learning: Trends, perspectives, and prospects.

Authors: M I Jordan; T M Mitchell
Journal: Science Date: 2015-07-17 Impact factor: 47.728

2. Artificial Intelligence in Nephrology: Core Concepts, Clinical Applications, and Perspectives.

Authors: Olivier Niel; Paul Bastard
Journal: Am J Kidney Dis Date: 2019-08-23 Impact factor: 8.860

3. Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics.

Authors: William T Tran; Katarzyna Jerzak; Fang-I Lu; Jonathan Klein; Sami Tabbarah; Andrew Lagree; Tina Wu; Ivan Rosado-Mendez; Ethan Law; Khadijeh Saednia; Ali Sadeghi-Naini
Journal: J Med Imaging Radiat Sci Date: 2019-08-22

Review 4. Artificial Intelligence in Obstetrics and Gynaecology: Is This the Way Forward?

Authors: Sonji Clarke; Michail Sideris; Elif Iliria Emin; Ece Emin; Apostolos Papalois; Fredric Willmott
Journal: In Vivo Date: 2019 Sep-Oct Impact factor: 2.155

5. Prediction of ⁹⁰Y Radioembolization Outcome from Pretherapeutic Factors with Random Survival Forests.

Authors: Michael Ingrisch; Franziska Schöppe; Karolin Paprottka; Matthias Fabritius; Frederik F Strobl; Enrico N De Toni; Harun Ilhan; Andrei Todica; Marlies Michl; Philipp Marius Paprottka
Journal: J Nucl Med Date: 2017-11-16 Impact factor: 10.057

Review 6. Radiogenomics and IR.

Authors: Alexander Lam; Kevin Bui; Eduardo Hernandez Rangel; Michael Nguyentat; Dayantha Fernando; Kari Nelson; Nadine Abi-Jaoudeh
Journal: J Vasc Interv Radiol Date: 2018-03-15 Impact factor: 3.464

7. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.

Authors: Ziad Obermeyer; Ezekiel J Emanuel
Journal: N Engl J Med Date: 2016-09-29 Impact factor: 91.245

8. Prospective Validation of 18F-FDG Brain PET Discriminant Analysis Methods in the Diagnosis of Amyotrophic Lateral Sclerosis.

Authors: Donatienne Van Weehaeghe; Jenny Ceccarini; Aline Delva; Wim Robberecht; Philip Van Damme; Koen Van Laere
Journal: J Nucl Med Date: 2016-03-03 Impact factor: 10.057

9. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.

Authors: Hoo-Chang Shin; Holger R Roth; Mingchen Gao; Le Lu; Ziyue Xu; Isabella Nogues; Jianhua Yao; Daniel Mollura; Ronald M Summers
Journal: IEEE Trans Med Imaging Date: 2016-02-11 Impact factor: 10.048

Artificial intelligence and radiomics in nuclear medicine: potentials and challenges.

Review 1. Machine learning: Trends, perspectives, and prospects.

2. Artificial Intelligence in Nephrology: Core Concepts, Clinical Applications, and Perspectives.

3. Personalized Breast Cancer Treatments Using Artificial Intelligence in Radiomics and Pathomics.

Review 4. Artificial Intelligence in Obstetrics and Gynaecology: Is This the Way Forward?

5. Prediction of ⁹⁰Y Radioembolization Outcome from Pretherapeutic Factors with Random Survival Forests.

Review 6. Radiogenomics and IR.

7. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine.

8. Prospective Validation of 18F-FDG Brain PET Discriminant Analysis Methods in the Diagnosis of Amyotrophic Lateral Sclerosis.

9. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning.

10. Radiomics: Images Are More than Pictures, They Are Data.

Review 1. Applications of artificial intelligence in nuclear medicine image generation.

2. Artificial Intelligence and Precision Medicine: A Perspective.

Review 3. Leveraging Data Science for a Personalized Haemodialysis.

Review 4. Artificial intelligence technologies in nuclear medicine.