Literature DB >> 31481587

Artificial Intelligence in Nuclear Medicine.

Felix Nensa1, Aydin Demircioglu2, Christoph Rischpler3.   

Abstract

Despite the great media attention for artificial intelligence (AI), for many health care professionals the term and the functioning of AI remain a "black box," leading to exaggerated expectations on the one hand and unfounded fears on the other. In this review, we provide a conceptual classification and a brief summary of the technical fundamentals of AI. Possible applications are discussed on the basis of a typical work flow in medical imaging, grouped by planning, scanning, interpretation, and reporting. The main limitations of current AI techniques, such as issues with interpretability or the need for large amounts of annotated data, are briefly addressed. Finally, we highlight the possible impact of AI on the nuclear medicine profession, the associated challenges and, last but not least, the opportunities.
© 2019 by the Society of Nuclear Medicine and Molecular Imaging.

Keywords:  artificial intelligence; deep learning; machine learning; medical imaging; nuclear medicine

Mesh:

Year:  2019        PMID: 31481587     DOI: 10.2967/jnumed.118.220590

Source DB:  PubMed          Journal:  J Nucl Med        ISSN: 0161-5505            Impact factor:   10.057


  21 in total

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

Authors:  Zhibiao Cheng; Junhai Wen; Gang Huang; Jianhua Yan
Journal:  Quant Imaging Med Surg       Date:  2021-06

Review 2.  Artificial intelligence in diagnostic imaging: impact on the radiography profession.

Authors:  Maryann Hardy; Hugh Harvey
Journal:  Br J Radiol       Date:  2019-12-16       Impact factor: 3.039

Review 3.  Application of artificial intelligence in nuclear medicine and molecular imaging: a review of current status and future perspectives for clinical translation.

Authors:  Dimitris Visvikis; Philippe Lambin; Kim Beuschau Mauridsen; Roland Hustinx; Michael Lassmann; Christoph Rischpler; Kuangyu Shi; Jan Pruim
Journal:  Eur J Nucl Med Mol Imaging       Date:  2022-07-09       Impact factor: 9.236

Review 4.  Artificial intelligence for nuclear medicine in oncology.

Authors:  Kenji Hirata; Hiroyuki Sugimori; Noriyuki Fujima; Takuya Toyonaga; Kohsuke Kudo
Journal:  Ann Nucl Med       Date:  2022-01-14       Impact factor: 2.668

Review 5.  Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review.

Authors:  Ioannis D Apostolopoulos; Nikolaos D Papathanasiou; Dimitris J Apostolopoulos; George S Panayiotakis
Journal:  Eur J Nucl Med Mol Imaging       Date:  2022-04-22       Impact factor: 10.057

6.  Deep learning-based detection of parathyroid adenoma by 99mTc-MIBI scintigraphy in patients with primary hyperparathyroidism.

Authors:  Atsushi Yoshida; Daiju Ueda; Shigeaki Higashiyama; Yutaka Katayama; Toshimasa Matsumoto; Takashi Yamanaga; Yukio Miki; Joji Kawabe
Journal:  Ann Nucl Med       Date:  2022-02-18       Impact factor: 2.258

7.  Usefulness of an artificial neural network for a beginner to achieve similar interpretations to an expert when examining myocardial perfusion images.

Authors:  A Chiba; T Kudo; R Ideguchi; M Altay; S Koga; T Yonekura; A Tsuneto; M Morikawa; S Ikeda; H Kawano; Y Koide; M Uetani; K Maemura
Journal:  Int J Cardiovasc Imaging       Date:  2021-03-11       Impact factor: 2.357

8.  A Glimpse on Trends and Characteristics of Recent Articles Published in the Korean Journal of Radiology.

Authors:  Yeon Hyeon Choe
Journal:  Korean J Radiol       Date:  2019-12       Impact factor: 3.500

Review 9.  Requirements and reliability of AI in the medical context.

Authors:  Yoganand Balagurunathan; Ross Mitchell; Issam El Naqa
Journal:  Phys Med       Date:  2021-03-13       Impact factor: 2.685

10.  Exciting Opportunities in Nuclear Medicine Imaging and Therapy.

Authors:  Constantin Lapa
Journal:  J Clin Med       Date:  2019-11-12       Impact factor: 4.241

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