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MeSH Review

Diagnosis, Computer-Assisted

 
 
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Disease relevance of Diagnosis, Computer-Assisted

 

High impact information on Diagnosis, Computer-Assisted

  • With an appropriate statistical description of various discriminate characteristics of both true and false candidates from the localized areas, an improved mass detection may be achieved in computer-assisted diagnosis (CAD) [3].
  • We propose that this user-friendly database is a potential tool for computer-assisted diagnosis of hematopoietic neoplasms [4].
  • Thanks to the worldwide accessibility of the Internet, the classification system offers a promising perspective towards modern computer-assisted diagnosis in both medicine and tele-medicine, for example and in particular, with respect to non-ophthalmic clinics or in communities where perimetric expertise is not readily available [5].
  • Towards the computerisation of ANIRCEF Headache Centres. Presentation of AIDA CEFALEE, a computer assisted diagnosis database for the management of headache patients [6].
  • This review discusses the techniques of computer-assisted diagnosis for thallium 201 myocardial-perfusion studies [7].
 

Gene context of Diagnosis, Computer-Assisted

 

Analytical, diagnostic and therapeutic context of Diagnosis, Computer-Assisted

References

  1. A computer aid for early diagnostic classification of jaundice (the COMIP program). Segaar, R.W., Wilson, J.H., Habbema, J.D., Hilden, J. Computer methods and programs in biomedicine. (1989) [Pubmed]
  2. Differential diagnosis of rheumatic diseases using conventional radiography. Kainberger, F., Peloschek, P., Langs, G., Boegl, K., Bischof, H. Best practice & research. Clinical rheumatology. (2004) [Pubmed]
  3. Computerized radiographic mass detection--part I: Lesion site selection by morphological enhancement and contextual segmentation. Li, H., Wang, Y., Liu, K.J., Lo, S.C., Freedman, M.T. IEEE transactions on medical imaging. (2001) [Pubmed]
  4. A relational database for diagnosis of hematopoietic neoplasms using immunophenotyping by flow cytometry. Nguyen, A.N., Milam, J.D., Johnson, K.A., Banez, E.I. Am. J. Clin. Pathol. (2000) [Pubmed]
  5. Neural attractor network for application in visual field data classification. Fink, W. Physics in medicine and biology. (2004) [Pubmed]
  6. Towards the computerisation of ANIRCEF Headache Centres. Presentation of AIDA CEFALEE, a computer assisted diagnosis database for the management of headache patients. De Simone, R., Marano, E., Bonavita, V. Neurol. Sci. (2004) [Pubmed]
  7. The use of computer-assisted diagnosis in cardiac-perfusion nuclear medicine studies: a review. Datz, F.L., Gabor, F.V., Christian, P.E., Gullberg, G.T., Menzel, C.E., Morton, K.A. Journal of digital imaging : the official journal of the Society for Computer Applications in Radiology. (1992) [Pubmed]
  8. Computer-assisted diagnosis of strabismus. Albicka, K., Metz, H.S., Albicki, A., Cohen, G. Journal of pediatric ophthalmology and strabismus. (1987) [Pubmed]
  9. Standardization for image characteristics in telemammography using genetic and nonlinear algorithms. Qian, W., Sankar, R., Song, X., Sun, X., Clark, R. Comput. Biol. Med. (2005) [Pubmed]
 
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