Literature DB >> 24482142

Clinical decision support systems in myocardial perfusion imaging.

Ernest V Garcia1, J Larry Klein, Andrew T Taylor.   

Abstract

Diagnostic imaging is becoming more complicated, physicians are also required to master an ever-expanding knowledge base and take into account an ever increasing amount of patient-specific clinical information while the time available to master this knowledge base, assemble the relevant clinical data, and apply it to specific tasks is steadily shrinking. Compounding these problems, there is an ever increasing number of aging "Baby Boomers" who are becoming patients coupled with a declining number of cardiac diagnosticians experienced in interpreting these studies. Hence, it is crucial that decision support tools be developed and implemented to assist physicians in interpreting studies at a faster rate and at the highest level of up-to-date expertise. Such tools will minimize subjectivity and intra- and inter-observer variation in image interpretation, help achieve a standardized high level of performance, and reduce healthcare costs. Presently, there are many decision support systems and approaches being developed and implemented to provide greater automation and to further objectify and standardize analysis, display, integration, interpretation, and reporting of myocardial perfusion SPECT and PET studies. This review focuses on these systems and approaches.

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Year:  2014        PMID: 24482142     DOI: 10.1007/s12350-014-9857-9

Source DB:  PubMed          Journal:  J Nucl Cardiol        ISSN: 1071-3581            Impact factor:   5.952


  45 in total

1.  Quantification of SPECT myocardial perfusion images: methodology and validation of the Yale-CQ method.

Authors:  Y H Liu; A J Sinusas; P DeMan; B L Zaret; F J Wackers
Journal:  J Nucl Cardiol       Date:  1999 Mar-Apr       Impact factor: 5.952

2.  Quantification of nuclear cardiac images: the Yale approach.

Authors:  Yi-Hwa Liu
Journal:  J Nucl Cardiol       Date:  2007-07       Impact factor: 5.952

3.  Quantitative analysis of tomographic stress thallium-201 myocardial scintigrams: a multicenter trial.

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Journal:  J Nucl Med       Date:  1990-07       Impact factor: 10.057

4.  Improved classifications of myocardial bull's-eye scintigrams with computer-based decision support system.

Authors:  D Lindahl; J Lanke; A Lundin; J Palmer; L Edenbrandt
Journal:  J Nucl Med       Date:  1999-01       Impact factor: 10.057

5.  Computer-assisted clinical decision-making.

Authors:  G A Gorry
Journal:  Methods Inf Med       Date:  1973-01       Impact factor: 2.176

6.  Diagnostic performance of an expert system for the interpretation of myocardial perfusion SPECT studies.

Authors:  E V Garcia; C D Cooke; R D Folks; C A Santana; E G Krawczynska; L De Braal; N F Ezquerra
Journal:  J Nucl Med       Date:  2001-08       Impact factor: 10.057

7.  Feasibility analysis of a case-based reasoning system for automated detection of coronary heart disease from myocardial scintigrams.

Authors:  M Haddad; K P Adlassnig; G Porenta
Journal:  Artif Intell Med       Date:  1997-01       Impact factor: 5.326

8.  Multicenter trial validation for quantitative analysis of same-day rest-stress technetium-99m-sestamibi myocardial tomograms.

Authors:  K F Van Train; E V Garcia; J Maddahi; J Areeda; C D Cooke; H Kiat; G Silagan; R Folks; J Friedman; L Matzer
Journal:  J Nucl Med       Date:  1994-04       Impact factor: 10.057

9.  Automated alignment and sizing of myocardial stress and rest scans to three-dimensional normal templates using an image registration algorithm.

Authors:  P J Slomka; G A Hurwitz; J Stephenson; T Cradduck
Journal:  J Nucl Med       Date:  1995-06       Impact factor: 10.057

10.  Quantitative rotational thallium-201 tomography for identifying and localizing coronary artery disease.

Authors:  E E DePasquale; A C Nody; E G DePuey; E V Garcia; G Pilcher; C Bredlau; G Roubin; A Gober; A Gruentzig; P D'Amato
Journal:  Circulation       Date:  1988-02       Impact factor: 29.690

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  4 in total

1.  Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images: a Japanese multicenter study.

Authors:  Kenichi Nakajima; Takashi Kudo; Tomoaki Nakata; Keisuke Kiso; Tokuo Kasai; Yasuyo Taniguchi; Shinro Matsuo; Mitsuru Momose; Masayasu Nakagawa; Masayoshi Sarai; Satoshi Hida; Hirokazu Tanaka; Kunihiko Yokoyama; Koichi Okuda; Lars Edenbrandt
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-09-26       Impact factor: 9.236

2.  Artificial neural network retrained to detect myocardial ischemia using a Japanese multicenter database.

Authors:  Kenichi Nakajima; Koichi Okuda; Satoru Watanabe; Shinro Matsuo; Seigo Kinuya; Karin Toth; Lars Edenbrandt
Journal:  Ann Nucl Med       Date:  2018-03-07       Impact factor: 2.668

3.  Evaluating machine learning models for sepsis prediction: A systematic review of methodologies.

Authors:  Hong-Fei Deng; Ming-Wei Sun; Yu Wang; Jun Zeng; Ting Yuan; Ting Li; Di-Huan Li; Wei Chen; Ping Zhou; Qi Wang; Hua Jiang
Journal:  iScience       Date:  2021-12-20

4.  Development, diagnostic performance, and interobserver agreement of a 18F-flurpiridaz PET automated perfusion quantitation system.

Authors:  René R Sevag Packard; C David Cooke; Kenneth F Van Train; John R Votaw; James W Sayre; Joel L Lazewatsky; Kelly M Champagne; Cesare Orlandi; Ernest V Garcia; Jamshid Maddahi
Journal:  J Nucl Cardiol       Date:  2020-09-07       Impact factor: 5.952

  4 in total

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