Literature DB >> 29502585

Role of Big Data and Machine Learning in Diagnostic Decision Support in Radiology.

Tanveer Syeda-Mahmood1.   

Abstract

The field of diagnostic decision support in radiology is undergoing rapid transformation with the availability of large amounts of patient data and the development of new artificial intelligence methods of machine learning such as deep learning. They hold the promise of providing imaging specialists with tools for improving the accuracy and efficiency of diagnosis and treatment. In this article, we will describe the growth of this field for radiology and outline general trends highlighting progress in the field of diagnostic decision support from the early days of rule-based expert systems to cognitive assistants of the modern era.
Copyright © 2018 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Diagnostic decision support; artificial intelligence; cognitive assistants; deep learning; knowledge and reasoning; machine learning; medical image analysis

Mesh:

Year:  2018        PMID: 29502585     DOI: 10.1016/j.jacr.2018.01.028

Source DB:  PubMed          Journal:  J Am Coll Radiol        ISSN: 1546-1440            Impact factor:   5.532


  13 in total

1.  Open access image repositories: high-quality data to enable machine learning research.

Authors:  F Prior; J Almeida; P Kathiravelu; T Kurc; K Smith; T J Fitzgerald; J Saltz
Journal:  Clin Radiol       Date:  2019-04-28       Impact factor: 2.350

2.  Medical students' attitude towards artificial intelligence: a multicentre survey.

Authors:  D Pinto Dos Santos; D Giese; S Brodehl; S H Chon; W Staab; R Kleinert; D Maintz; B Baeßler
Journal:  Eur Radiol       Date:  2018-07-06       Impact factor: 5.315

3.  Can Big Data guide prognosis and clinical decisions in epilepsy?

Authors:  Xiaojin Li; Licong Cui; Guo-Qiang Zhang; Samden D Lhatoo
Journal:  Epilepsia       Date:  2021-02-02       Impact factor: 5.864

4.  Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings.

Authors:  Máté E Maros; Chang Gyu Cho; Andreas G Junge; Benedikt Kämpgen; Victor Saase; Fabian Siegel; Frederik Trinkmann; Thomas Ganslandt; Christoph Groden; Holger Wenz
Journal:  Sci Rep       Date:  2021-03-09       Impact factor: 4.379

5.  Convolutional neural network for cell classification using microscope images of intracellular actin networks.

Authors:  Ronald Wihal Oei; Guanqun Hou; Fuhai Liu; Jin Zhong; Jiewen Zhang; Zhaoyi An; Luping Xu; Yujiu Yang
Journal:  PLoS One       Date:  2019-03-13       Impact factor: 3.240

6.  Effectiveness of Deep Learning Algorithms to Determine Laterality in Radiographs.

Authors:  Ross W Filice; Shelby K Frantz
Journal:  J Digit Imaging       Date:  2019-08       Impact factor: 4.056

7.  Supervised machine learning for automated classification of human Wharton's Jelly cells and mechanosensory hair cells.

Authors:  Abihith Kothapalli; Hinrich Staecker; Adam J Mellott
Journal:  PLoS One       Date:  2021-01-08       Impact factor: 3.240

8.  DICOM re-encoding of volumetrically annotated Lung Imaging Database Consortium (LIDC) nodules.

Authors:  Andrey Fedorov; Matthew Hancock; David Clunie; Mathias Brochhausen; Jonathan Bona; Justin Kirby; John Freymann; Steve Pieper; Hugo J W L Aerts; Ron Kikinis; Fred Prior
Journal:  Med Phys       Date:  2020-09-06       Impact factor: 4.071

9.  Risk prediction for malignant intraductal papillary mucinous neoplasm of the pancreas: logistic regression versus machine learning.

Authors:  Jae Seung Kang; Chanhee Lee; Wookyeong Song; Wonho Choo; Seungyeoun Lee; Sungyoung Lee; Youngmin Han; Claudio Bassi; Roberto Salvia; Giovanni Marchegiani; Cristopher L Wolfgang; Jin He; Alex B Blair; Michael D Kluger; Gloria H Su; Song Cheol Kim; Ki-Byung Song; Masakazu Yamamoto; Ryota Higuchi; Takashi Hatori; Ching-Yao Yang; Hiroki Yamaue; Seiko Hirono; Sohei Satoi; Tsutomu Fujii; Satoshi Hirano; Wenhui Lou; Yasushi Hashimoto; Yasuhiro Shimizu; Marco Del Chiaro; Roberto Valente; Matthias Lohr; Dong Wook Choi; Seong Ho Choi; Jin Seok Heo; Fuyuhiko Motoi; Ippei Matsumoto; Woo Jung Lee; Chang Moo Kang; Yi-Ming Shyr; Shin-E Wang; Ho-Seong Han; Yoo-Seok Yoon; Marc G Besselink; Nadine C M van Huijgevoort; Masayuki Sho; Hiroaki Nagano; Sang Geol Kim; Goro Honda; Yinmo Yang; Hee Chul Yu; Jae Do Yang; Jun Chul Chung; Yuichi Nagakawa; Hyung Il Seo; Yoo Jin Choi; Yoonhyeong Byun; Hongbeom Kim; Wooil Kwon; Taesung Park; Jin-Young Jang
Journal:  Sci Rep       Date:  2020-11-18       Impact factor: 4.379

Review 10.  Reviewing the relationship between machines and radiology: the application of artificial intelligence.

Authors:  Rani Ahmad
Journal:  Acta Radiol Open       Date:  2021-02-09
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