Literature DB >> 31398274

Artificial Intelligence in Musculoskeletal Imaging: A Paradigm Shift.

Joseph E Burns1, Jianhua Yao2, Ronald M Summers2.   

Abstract

Artificial intelligence is upending many of our assumptions about the ability of computers to detect and diagnose diseases on medical images. Deep learning, a recent innovation in artificial intelligence, has shown the ability to interpret medical images with sensitivities and specificities at or near that of skilled clinicians for some applications. In this review, we summarize the history of artificial intelligence, present some recent research advances, and speculate about the potential revolutionary clinical impact of the latest computer techniques for bone and muscle imaging.
© 2019 American Society for Bone and Mineral Research. Published 2019. This article is a U.S. Government work and is in the public domain in the USA. Published 2019. This article is a U.S. Government work and is in the public domain in the USA.

Keywords:  ANALYSIS/QUANTITATION OF BONE; CANCER; RADIOLOGY; SKELETAL MUSCLE

Mesh:

Year:  2019        PMID: 31398274     DOI: 10.1002/jbmr.3849

Source DB:  PubMed          Journal:  J Bone Miner Res        ISSN: 0884-0431            Impact factor:   6.741


  6 in total

1.  Artificial intelligence in musculoskeletal oncological radiology.

Authors:  Matjaz Vogrin; Teodor Trojner; Robi Kelc
Journal:  Radiol Oncol       Date:  2020-11-10       Impact factor: 2.991

Review 2.  Artificial Intelligence Explained for Nonexperts.

Authors:  Narges Razavian; Florian Knoll; Krzysztof J Geras
Journal:  Semin Musculoskelet Radiol       Date:  2020-01-28       Impact factor: 1.777

Review 3.  Real-world analysis of artificial intelligence in musculoskeletal trauma.

Authors:  Pranav Ajmera; Amit Kharat; Rajesh Botchu; Harun Gupta; Viraj Kulkarni
Journal:  J Clin Orthop Trauma       Date:  2021-08-27

4.  Artificial intelligence in musculoskeletal oncological radiology.

Authors:  Matjaz Vogrin; Teodor Trojner; Robi Kelc
Journal:  Radiol Oncol       Date:  2020-11-10       Impact factor: 2.991

5.  Automated detection of the contrast phase in MDCT by an artificial neural network improves the accuracy of opportunistic bone mineral density measurements.

Authors:  Sebastian Rühling; Fernando Navarro; Anjany Sekuboyina; Malek El Husseini; Thomas Baum; Bjoern Menze; Rickmer Braren; Claus Zimmer; Jan S Kirschke
Journal:  Eur Radiol       Date:  2021-10-23       Impact factor: 5.315

6.  Multi-scanner and multi-modal lumbar vertebral body and intervertebral disc segmentation database.

Authors:  Yasmina Al Khalil; Edoardo A Becherucci; Jan S Kirschke; Dimitrios C Karampinos; Marcel Breeuwer; Thomas Baum; Nico Sollmann
Journal:  Sci Data       Date:  2022-03-23       Impact factor: 6.444

  6 in total

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