Literature DB >> 20965900

Decision support systems for clinical radiological practice -- towards the next generation.

S M Stivaros1, A Gledson, G Nenadic, X-J Zeng, J Keane, A Jackson.   

Abstract

The huge amount of information that needs to be assimilated in order to keep pace with the continued advances in modern medical practice can form an insurmountable obstacle to the individual clinician. Within radiology, the recent development of quantitative imaging techniques, such as perfusion imaging, and the development of imaging-based biomarkers in modern therapeutic assessment has highlighted the need for computer systems to provide the radiological community with support for academic as well as clinical/translational applications. This article provides an overview of the underlying design and functionality of radiological decision support systems with examples tracing the development and evolution of such systems over the past 40 years. More importantly, we discuss the specific design, performance and usage characteristics that previous systems have highlighted as being necessary for clinical uptake and routine use. Additionally, we have identified particular failings in our current methodologies for data dissemination within the medical domain that must be overcome if the next generation of decision support systems is to be implemented successfully.

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Year:  2010        PMID: 20965900      PMCID: PMC3473729          DOI: 10.1259/bjr/33620087

Source DB:  PubMed          Journal:  Br J Radiol        ISSN: 0007-1285            Impact factor:   3.039


  40 in total

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Journal:  Stat Med       Date:  1985 Jul-Sep       Impact factor: 2.373

6.  Evaluation of computer advisor in the interpretation of CT images of the head.

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7.  Simulation studies of data classification by artificial neural networks: potential applications in medical imaging and decision making.

Authors:  Y Wu; K Doi; C E Metz; N Asada; M L Giger
Journal:  J Digit Imaging       Date:  1993-05       Impact factor: 4.056

8.  Using a Bayesian network to predict the probability and type of breast cancer represented by microcalcifications on mammography.

Authors:  Elizabeth S Burnside; Daniel L Rubin; Ross D Shachter
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Review 9.  Changing concepts of cerebrospinal fluid hydrodynamics: role of phase-contrast magnetic resonance imaging and implications for cerebral microvascular disease.

Authors:  Stavros Michael Stivaros; Alan Jackson
Journal:  Neurotherapeutics       Date:  2007-07       Impact factor: 7.620

10.  Magnetic resonance perfusion imaging in neuro-oncology.

Authors:  Alan Jackson; James O'Connor; Gerard Thompson; Samantha Mills
Journal:  Cancer Imaging       Date:  2008-10-13       Impact factor: 3.909

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  8 in total

Review 1.  The Lancet Commission on diagnostics: transforming access to diagnostics.

Authors:  Kenneth A Fleming; Susan Horton; Michael L Wilson; Rifat Atun; Kristen DeStigter; John Flanigan; Shahin Sayed; Pierrick Adam; Bertha Aguilar; Savvas Andronikou; Catharina Boehme; William Cherniak; Annie Ny Cheung; Bernice Dahn; Lluis Donoso-Bach; Tania Douglas; Patricia Garcia; Sarwat Hussain; Hari S Iyer; Mikashmi Kohli; Alain B Labrique; Lai-Meng Looi; John G Meara; John Nkengasong; Madhukar Pai; Kara-Lee Pool; Kaushik Ramaiya; Lee Schroeder; Devanshi Shah; Richard Sullivan; Bien-Soo Tan; Kamini Walia
Journal:  Lancet       Date:  2021-10-06       Impact factor: 79.321

2.  Using automatically extracted information from mammography reports for decision-support.

Authors:  Selen Bozkurt; Francisco Gimenez; Elizabeth S Burnside; Kemal H Gulkesen; Daniel L Rubin
Journal:  J Biomed Inform       Date:  2016-07-04       Impact factor: 6.317

3.  Cervical vertebral maturation assessment on lateral cephalometric radiographs using artificial intelligence: comparison of machine learning classifier models.

Authors:  Hakan Amasya; Derya Yildirim; Turgay Aydogan; Nazan Kemaloglu; Kaan Orhan
Journal:  Dentomaxillofac Radiol       Date:  2020-03-09       Impact factor: 2.419

Review 4.  Biomedical informatics for computer-aided decision support systems: a survey.

Authors:  Ashwin Belle; Mark A Kon; Kayvan Najarian
Journal:  ScientificWorldJournal       Date:  2013-02-04

5.  Random Forest ensembles for detection and prediction of Alzheimer's disease with a good between-cohort robustness.

Authors:  A V Lebedev; E Westman; G J P Van Westen; M G Kramberger; A Lundervold; D Aarsland; H Soininen; I Kłoszewska; P Mecocci; M Tsolaki; B Vellas; S Lovestone; A Simmons
Journal:  Neuroimage Clin       Date:  2014-08-28       Impact factor: 4.881

Review 6.  The role of artificial intelligence in paediatric neuroradiology.

Authors:  Catherine Pringle; John-Paul Kilday; Ian Kamaly-Asl; Stavros Michael Stivaros
Journal:  Pediatr Radiol       Date:  2022-03-26

7.  Automated Modular Magnetic Resonance Imaging Clinical Decision Support System (MIROR): An Application in Pediatric Cancer Diagnosis.

Authors:  Niloufar Zarinabad; Emma M Meeus; Karen Manias; Katharine Foster; Andrew Peet
Journal:  JMIR Med Inform       Date:  2018-05-02

8.  A Bayesian Network Analysis of the Diagnostic Process and Its Accuracy to Determine How Clinicians Estimate Cardiac Function in Critically Ill Patients: Prospective Observational Cohort Study.

Authors:  Thomas Kaufmann; José Castela Forte; Bart Hiemstra; Marco A Wiering; Marco Grzegorczyk; Anne H Epema; Iwan C C van der Horst
Journal:  JMIR Med Inform       Date:  2019-10-30
  8 in total

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