Literature DB >> 29679221

Machine learning for nuclear cardiology: The way forward.

Sirish Shrestha1, Partho P Sengupta2.   

Abstract

Entities:  

Year:  2018        PMID： 29679221      PMCID： PMC7061628          DOI： 10.1007/s12350-018-1284-x

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


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  17 in total

1.  Evaluation of computer-aided detection and diagnosis systems.

Authors:  Nicholas Petrick; Berkman Sahiner; Samuel G Armato; Alberto Bert; Loredana Correale; Silvia Delsanto; Matthew T Freedman; David Fryd; David Gur; Lubomir Hadjiiski; Zhimin Huo; Yulei Jiang; Lia Morra; Sophie Paquerault; Vikas Raykar; Frank Samuelson; Ronald M Summers; Georgia Tourassi; Hiroyuki Yoshida; Bin Zheng; Chuan Zhou; Heang-Ping Chan
Journal:  Med Phys       Date:  2013-08       Impact factor: 4.071

2.  An open-source framework of neural networks for diagnosis of coronary artery disease from myocardial perfusion SPECT.

Authors:  Levent A Guner; Nese Ilgin Karabacak; Ozgur U Akdemir; Pinar Senkul Karagoz; Sinan A Kocaman; Atiye Cengel; Mustafa Unlu
Journal:  J Nucl Cardiol       Date:  2010-03-04       Impact factor: 5.952

3.  Image reconstruction by domain-transform manifold learning.

Authors:  Bo Zhu; Jeremiah Z Liu; Stephen F Cauley; Bruce R Rosen; Matthew S Rosen
Journal:  Nature       Date:  2018-03-21       Impact factor: 49.962

Review 4.  Machine learning in cardiovascular medicine: are we there yet?

Authors:  Khader Shameer; Kipp W Johnson; Benjamin S Glicksberg; Joel T Dudley; Partho P Sengupta
Journal:  Heart       Date:  2018-01-19       Impact factor: 5.994

Review 5.  Chronic coronary artery disease: diagnosis and management.

Authors:  Andrew Cassar; David R Holmes; Charanjit S Rihal; Bernard J Gersh
Journal:  Mayo Clin Proc       Date:  2009-12       Impact factor: 7.616

6.  Improved accuracy of myocardial perfusion SPECT for detection of coronary artery disease by machine learning in a large population.

Authors:  Reza Arsanjani; Yuan Xu; Damini Dey; Vishal Vahistha; Aryeh Shalev; Rine Nakanishi; Sean Hayes; Mathews Fish; Daniel Berman; Guido Germano; Piotr J Slomka
Journal:  J Nucl Cardiol       Date:  2013-05-24       Impact factor: 5.952

7.  Prognostic Value of Combined Clinical and Myocardial Perfusion Imaging Data Using Machine Learning.

Authors:  Julian Betancur; Yuka Otaki; Manish Motwani; Mathews B Fish; Mark Lemley; Damini Dey; Heidi Gransar; Balaji Tamarappoo; Guido Germano; Tali Sharir; Daniel S Berman; Piotr J Slomka
Journal:  JACC Cardiovasc Imaging       Date:  2017-10-18

8.  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

9.  The evolving roles of nuclear cardiology.

Authors:  Andrea De Lorenzo
Journal:  Curr Cardiol Rev       Date:  2009-01

10.  The FAIR Guiding Principles for scientific data management and stewardship.

Authors:  Mark D Wilkinson; Michel Dumontier; I Jsbrand Jan Aalbersberg; Gabrielle Appleton; Myles Axton; Arie Baak; Niklas Blomberg; Jan-Willem Boiten; Luiz Bonino da Silva Santos; Philip E Bourne; Jildau Bouwman; Anthony J Brookes; Tim Clark; Mercè Crosas; Ingrid Dillo; Olivier Dumon; Scott Edmunds; Chris T Evelo; Richard Finkers; Alejandra Gonzalez-Beltran; Alasdair J G Gray; Paul Groth; Carole Goble; Jeffrey S Grethe; Jaap Heringa; Peter A C 't Hoen; Rob Hooft; Tobias Kuhn; Ruben Kok; Joost Kok; Scott J Lusher; Maryann E Martone; Albert Mons; Abel L Packer; Bengt Persson; Philippe Rocca-Serra; Marco Roos; Rene van Schaik; Susanna-Assunta Sansone; Erik Schultes; Thierry Sengstag; Ted Slater; George Strawn; Morris A Swertz; Mark Thompson; Johan van der Lei; Erik van Mulligen; Jan Velterop; Andra Waagmeester; Peter Wittenburg; Katherine Wolstencroft; Jun Zhao; Barend Mons
Journal:  Sci Data       Date:  2016-03-15       Impact factor: 6.444
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  6 in total

Review 1.  Artificial Intelligence in Cardiovascular Medicine.

Authors:  Karthik Seetharam; Sirish Shrestha; Partho P Sengupta
Journal:  Curr Treat Options Cardiovasc Med       Date:  2019-05-14

2.  A methodological framework for AI-assisted diagnosis of active aortitis using radiomic analysis of FDG PET-CT images: Initial analysis.

Authors:  Lisa Duff; Andrew F Scarsbrook; Sarah L Mackie; Russell Frood; Marc Bailey; Ann W Morgan; Charalampos Tsoumpas
Journal:  J Nucl Cardiol       Date:  2022-03-23       Impact factor: 5.952

Review 3.  Applications of Machine Learning in Cardiology.

Authors:  Karthik Seetharam; Sudarshan Balla; Christopher Bianco; Jim Cheung; Roman Pachulski; Deepak Asti; Nikil Nalluri; Astha Tejpal; Parvez Mir; Jilan Shah; Premila Bhat; Tanveer Mir; Yasmin Hamirani
Journal:  Cardiol Ther       Date:  2022-07-12

4.  Preliminary Radiogenomic Evidence for the Prediction of Metastasis and Chemotherapy Response in Pediatric Patients with Osteosarcoma Using 18F-FDF PET/CT, EZRIN and KI67.

Authors:  Byung-Chul Kim; Jingyu Kim; Kangsan Kim; Byung Hyun Byun; Ilhan Lim; Chang-Bae Kong; Won Seok Song; Jae-Soo Koh; Sang-Keun Woo
Journal:  Cancers (Basel)       Date:  2021-05-28       Impact factor: 6.639

Review 5.  Cardiac magnetic resonance imaging: the future is bright.

Authors:  Karthik Seetharam; Stamatios Lerakis
Journal:  F1000Res       Date:  2019-09-13

6.  Nuclear cardiac imaging between implementation and globalization: The key role of integration.

Authors:  Alberto Cuocolo; Carmela Nappi; Wanda Acampa; Mario Petretta
Journal:  J Nucl Cardiol       Date:  2021-04-30       Impact factor: 5.952
  6 in total

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