Literature DB >> 2299705

Computer-aided diagnosis in chest radiology.

H MacMahon1, K Doi, H P Chan, M L Giger, S Katsuragawa, N Nakamori.   

Abstract

Digital radiography offers several important advantages over conventional systems, including abilities for image manipulation, transmission, and storage. In the long term, however, the unique ability to apply artificial intelligence techniques for automated detection and quantitation of disease may have an even greater impact on radiologic practice. Although CAD is still in its infancy, the results of several recent studies clearly indicate a major potential for the future. The concept of using computers to analyze medical images is not new, but recent advances in computer technology together with progress in implementing practical digital radiography systems have stimulated research efforts in this exciting field. Several facets of CAD are presently being developed at the University of Chicago and elsewhere for application in chest radiology as well as in mammography and vascular imaging. To date, investigators have focused on a limited number of subjects that have been, by their nature, particularly suitable for computer analysis. There is no aspect of radiologic diagnosis that could not potentially benefit from this approach, however. The ultimate goal of these endeavors is to provide a system for comprehensive automated image analysis, the results of which could be accepted or modified at the discretion of the radiologist.

Mesh:

Year:  1990        PMID: 2299705     DOI: 10.1097/00005382-199001000-00011

Source DB:  PubMed          Journal:  J Thorac Imaging        ISSN: 0883-5993            Impact factor:   3.000


  9 in total

1.  Hardware and software requirements for a picture archiving and communication system's diagnostic workstations.

Authors:  D R Haynor; D V Smith; H W Park; Y Kim
Journal:  J Digit Imaging       Date:  1992-05       Impact factor: 4.056

Review 2.  Medical image databases: a content-based retrieval approach.

Authors:  H D Tagare; C C Jaffe; J Duncan
Journal:  J Am Med Inform Assoc       Date:  1997 May-Jun       Impact factor: 4.497

Review 3.  Potential clinical impact of advanced imaging and computer-aided diagnosis in chest radiology: importance of radiologist's role and successful observer study.

Authors:  Feng Li
Journal:  Radiol Phys Technol       Date:  2015-05-17

Review 4.  Potential usefulness of digital imaging in clinical diagnostic radiology: computer-aided diagnosis.

Authors:  K Doi; M L Giger; R M Nishikawa; K R Hoffmann; H MacMahon; R A Schmidt
Journal:  J Digit Imaging       Date:  1995-02       Impact factor: 4.056

Review 5.  Digital chest radiography at the University of Chicago: present status and future plans.

Authors:  H MacMahon
Journal:  J Digit Imaging       Date:  1995-02       Impact factor: 4.056

6.  Classification of normal and abnormal lungs with interstitial diseases by rule-based method and artificial neural networks.

Authors:  S Katsuragawa; K Doi; H MacMahon; L Monnier-Cholley; T Ishida; T Kobayashi
Journal:  J Digit Imaging       Date:  1997-08       Impact factor: 4.056

7.  Quantitative analysis of geometric-pattern features of interstitial infiltrates in digital chest radiographs: preliminary results.

Authors:  S Katsuragawa; K Doi; H MacMahon; L Monnier-Cholley; J Morishita; T Ishida
Journal:  J Digit Imaging       Date:  1996-08       Impact factor: 4.056

Review 8.  Artificial intelligence in medicine and male infertility.

Authors:  D J Lamb; C S Niederberger
Journal:  World J Urol       Date:  1993       Impact factor: 4.226

Review 9.  Artificial Intelligence for MR Image Reconstruction: An Overview for Clinicians.

Authors:  Dana J Lin; Patricia M Johnson; Florian Knoll; Yvonne W Lui
Journal:  J Magn Reson Imaging       Date:  2020-02-12       Impact factor: 4.813

  9 in total

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