Literature DB >> 8316671

An "intelligent" workstation for computer-aided diagnosis.

M L Giger1, K Doi, H MacMahon, R M Nishikawa, K R Hoffmann, C J Vyborny, R A Schmidt, H Jia, K Abe, X Chen.   

Abstract

Computer-aided diagnosis (CAD) involves a computerized analysis of radiographs that is used as a "second opinion" by the radiologist. The approach presented incorporates computer vision and artificial intelligence techniques and includes schemes for the analysis of lung nodules, interstitial infiltrates, and cardiomegaly seen on chest radiographs; masses and clustered microcalcifications on mammograms; and stenoses and blood flow on angiograms. The demonstration of various CAD schemes in chest radiography and mammography on a six-monitor workstation simulates one possible clinical implementation of CAD in radiology. Whether soft- or hard-copy display media are used, the radiologist can refer to the CAD results and still use the original radiograph for the final diagnosis. Although initial impressions of this simulated "intelligent" workstation are encouraging, CAD is still in a preliminary stage of development. Various methods for effectively and efficiently integrating CAD into a clinical radiology department are being investigated.

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Year:  1993        PMID: 8316671     DOI: 10.1148/radiographics.13.3.8316671

Source DB:  PubMed          Journal:  Radiographics        ISSN: 0271-5333            Impact factor:   5.333


  4 in total

Review 1.  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 2.  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

3.  The role of object representation in the design of the intelligent radiology workstation.

Authors:  K J Macura; R T Macura
Journal:  Proc Annu Symp Comput Appl Med Care       Date:  1994

4.  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

  4 in total

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