Literature DB >> 34809867

Artificial Intelligence and Positron Emission Tomography Imaging Workflow:: Technologists' Perspective.

Cheryl Beegle1, Navid Hasani2, Roberto Maass-Moreno1, Babak Saboury3, Eliot Siegel4.   

Abstract

Artificial intelligence (AI) can enhance the efficiency of medical imaging quality control and clinical documentation, provide clinical decision support, and increase image acquisition and processing quality. A clear understanding of the basic tenets of these technologies and their impact will enable nuclear medicine technologists to train for performing advanced imaging tasks. AI-enabled medical devices' anticipated role and impact on routine nuclear medicine workflow (scheduling, quality control, check-in, radiotracer injection, waiting room, image planning, image acquisition, image post-processing) is reviewed in this article. With the assistance of AI, newly compiled patient imaging data can be customized to encompass personalized risk assessments of patients' disease burden, along with the development of individualized treatment plans. Nuclear medicine technologists will continue to play a crucial role on the medical team, collaborating with patients and radiologists to improve each patient's imaging experience and supervising the performance of integrated AI applications.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Imaging workflow; Medical imaging; Nuclear medicine technologists

Mesh:

Year:  2022        PMID: 34809867      PMCID: PMC8797670          DOI: 10.1016/j.cpet.2021.09.008

Source DB:  PubMed          Journal:  PET Clin        ISSN: 1556-8598


  30 in total

1.  Automated image quality evaluation of T2 -weighted liver MRI utilizing deep learning architecture.

Authors:  Steven J Esses; Xiaoguang Lu; Tiejun Zhao; Krishna Shanbhogue; Bari Dane; Mary Bruno; Hersh Chandarana
Journal:  J Magn Reson Imaging       Date:  2017-06-03       Impact factor: 4.813

2.  Improving Workflow Efficiency for Mammography Using Machine Learning.

Authors:  Trent Kyono; Fiona J Gilbert; Mihaela van der Schaar
Journal:  J Am Coll Radiol       Date:  2019-05-30       Impact factor: 5.532

Review 3.  The first use of artificial intelligence (AI) in the ER: triage not diagnosis.

Authors:  Edmund M Weisberg; Linda C Chu; Elliot K Fishman
Journal:  Emerg Radiol       Date:  2020-07-08

Review 4.  Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement.

Authors:  Juan Liu; Masoud Malekzadeh; Niloufar Mirian; Tzu-An Song; Chi Liu; Joyita Dutta
Journal:  PET Clin       Date:  2021-10

5.  Trends in Use of Medical Imaging in US Health Care Systems and in Ontario, Canada, 2000-2016.

Authors:  Rebecca Smith-Bindman; Marilyn L Kwan; Emily C Marlow; Mary Kay Theis; Wesley Bolch; Stephanie Y Cheng; Erin J A Bowles; James R Duncan; Robert T Greenlee; Lawrence H Kushi; Jason D Pole; Alanna K Rahm; Natasha K Stout; Sheila Weinmann; Diana L Miglioretti
Journal:  JAMA       Date:  2019-09-03       Impact factor: 157.335

6.  AI-RADS: An Artificial Intelligence Curriculum for Residents.

Authors:  Alexander L Lindqwister; Saeed Hassanpour; Petra J Lewis; Jessica M Sin
Journal:  Acad Radiol       Date:  2020-10-15       Impact factor: 3.173

7.  Radiographers' perspectives on the emerging integration of artificial intelligence into diagnostic imaging: The Ghana study.

Authors:  Benard O Botwe; William K Antwi; Samuel Arkoh; Theophilus N Akudjedu
Journal:  J Med Radiat Sci       Date:  2021-02-14

8.  Artificial intelligence in medical imaging practice in Africa: a qualitative content analysis study of radiographers' perspectives.

Authors:  William Kwadwo Antwi; Theophilus N Akudjedu; Benard Ohene Botwe
Journal:  Insights Imaging       Date:  2021-06-16

9.  Data Integration for Future Medicine (DIFUTURE).

Authors:  Fabian Prasser; Oliver Kohlbacher; Ulrich Mansmann; Bernhard Bauer; Klaus A Kuhn
Journal:  Methods Inf Med       Date:  2018-07-17       Impact factor: 2.176

10.  Introducing Artificial Intelligence Training in Medical Education.

Authors:  Ketan Paranjape; Michiel Schinkel; Rishi Nannan Panday; Josip Car; Prabath Nanayakkara
Journal:  JMIR Med Educ       Date:  2019-12-03
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.