Literature DB >> 34089338

Musculoskeletal trauma and artificial intelligence: current trends and projections.

Olga Laur1, Benjamin Wang2.   

Abstract

Musculoskeletal trauma accounts for a significant fraction of emergency department visits and patients seeking urgent care, with a high financial cost to society. Diagnostic imaging is indispensable in the workup and management of trauma patients. However, diagnostic imaging represents a complex multifaceted system, with many aspects of its workflow prone to inefficiencies or human error. Recent technological innovations in artificial intelligence and machine learning have shown promise to revolutionize our systems for providing medical care to patients. This review will provide a general overview of the current state of artificial intelligence and machine learning applications in different aspects of trauma imaging and provide a vision for how such applications could be leveraged to enhance our diagnostic imaging systems and optimize patient outcomes.
© 2021. ISS.

Entities:  

Keywords:  Artificial intelligence; Body composition analysis; Deep learning; Geriatric trauma; Machine learning; Musculoskeletal trauma; Trauma imaging

Mesh:

Year:  2021        PMID: 34089338     DOI: 10.1007/s00256-021-03824-6

Source DB:  PubMed          Journal:  Skeletal Radiol        ISSN: 0364-2348            Impact factor:   2.199


  41 in total

Review 1.  Radiology for Trauma and the General Surgeon.

Authors:  Patrick K Kim
Journal:  Surg Clin North Am       Date:  2017-08-09       Impact factor: 2.741

Review 2.  Surgical Management of Musculoskeletal Trauma.

Authors:  Daniel J Stinner; Dafydd Edwards
Journal:  Surg Clin North Am       Date:  2017-10       Impact factor: 2.741

Review 3.  Deep learning.

Authors:  Yann LeCun; Yoshua Bengio; Geoffrey Hinton
Journal:  Nature       Date:  2015-05-28       Impact factor: 49.962

Review 4.  Artificial Intelligence in Musculoskeletal Imaging: Review of Current Literature, Challenges, and Trends.

Authors:  Anna Hirschmann; Joshy Cyriac; Bram Stieltjes; Tobias Kober; Jonas Richiardi; Patrick Omoumi
Journal:  Semin Musculoskelet Radiol       Date:  2019-06-04       Impact factor: 1.777

Review 5.  Current applications and future directions of deep learning in musculoskeletal radiology.

Authors:  Pauley Chea; Jacob C Mandell
Journal:  Skeletal Radiol       Date:  2019-08-04       Impact factor: 2.199

Review 6.  Convolutional Neural Networks for Radiologic Images: A Radiologist's Guide.

Authors:  Shelly Soffer; Avi Ben-Cohen; Orit Shimon; Michal Marianne Amitai; Hayit Greenspan; Eyal Klang
Journal:  Radiology       Date:  2019-01-29       Impact factor: 11.105

7.  Addressing Burnout in Radiologists.

Authors:  Alison L Chetlen; Tiffany L Chan; David H Ballard; L Alexandre Frigini; Andrea Hildebrand; Shannon Kim; James M Brian; Elizabeth A Krupinski; Dhakshinamoorthy Ganeshan
Journal:  Acad Radiol       Date:  2018-07-31       Impact factor: 3.173

8.  Understanding and Confronting Our Mistakes: The Epidemiology of Error in Radiology and Strategies for Error Reduction.

Authors:  Michael A Bruno; Eric A Walker; Hani H Abujudeh
Journal:  Radiographics       Date:  2015-10       Impact factor: 5.333

9.  The effect of scan interval and bolus length on the quantitative accuracy of cerebral computed tomography perfusion analysis using a hollow-fiber phantom.

Authors:  Hiroyuki Hashimoto; Kazufumi Suzuki; Eiji Okaniwa; Hiroshi Iimura; Kayoko Abe; Shuji Sakai
Journal:  Radiol Phys Technol       Date:  2017-10-16

10.  Errors in fracture diagnoses in the emergency department--characteristics of patients and diurnal variation.

Authors:  Peter Hallas; Trond Ellingsen
Journal:  BMC Emerg Med       Date:  2006-02-16
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  1 in total

Review 1.  The current role of the virtual elements of artificial intelligence in total knee arthroplasty.

Authors:  E Carlos Rodríguez-Merchán
Journal:  EFORT Open Rev       Date:  2022-07-05
  1 in total

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