Biomedical Engineering Reference
In-Depth Information
12.5 Conclusions
Computer-aided diagnosis has become a part of clinical work in the detection of
breast cancer by means of mammograms or lung nodules by means of CT, but is still
in the infancy of its full potential for applications to many different types of lesions
obtained with various modalities, for instance the bone erosion with MRI. In that
sense, the RheumaSCORE software is a good starting point.
The current trend is the integration of CAD system into PACS, as a package for
detection of lesions and also for differential diagnosis. CAD will be employed as a
useful tool for diagnostic examinations in daily clinical work. The success of CAD-
supported analysis processes depends on the capabilities of automated solutions to
simulate and improve what physicians and radiologists do when they inspect digital
data. The key challenges are:
software applications should be able to
identify and measure
clinical practice
parameters based on the same criteria used by physicians;
retrieval of
similar
clinical cases should be based on ontology-based techniques
in order to speed up the diagnosis process and to support comparative analysis
among known cases;
gathering information about specific patients in a database linked to the CAD
system should ease the evaluation of the
follow-up
in order to highlight temporal
trends of pathology markers, possibly depending on current therapy.
Acknowledgments
This work is supported by the FP7Marie Curie Initial Training Network “Mul-
tiScaleHuman”: Multi-scale Biological Modalities for Physiological Human Articulation (2011-
2015), contract MRTN-CT-2011-289897. Softeco wishes to thank Esaote Spa and DIMI (Dipar-
timento di Medicina Interna, Clinica Reumatologica, Università degli Studi di Genova) for their
collaboration. The RheumaSCORE software has been developed within the P.O.R. Liguria FESR
(2007-2013)—Asse 1 “Innovazione e competitività”—Bando Azione 1.2.2—Progetto SIDARMA.
References
1. Doi, K., Giger, M. L., MacMahon, H., et al. (1992). Computer-aided diagnosis: Development
of automated schemes for quantitative analysis of radiographic images.
Sem Ultrasound CT
MR, 13
(2), 140-152.
2. Doi, K., Giger, M. L., Nishikawa, R. M., Hoffmann, K. R., MacMahon, H., Schmidt, R. A., et al.
(1993). Digital radiography: A useful clinical tool for computer-aided diagnosis by quantitative
analysis of radiographic images.
Acta Radiologica
,
34
, 426-439.
3. Doi, K., MacMahon, H., Giger, M. L., & Hoffmann, K. R. (Eds.), (1999).
Computer aided
diagnosis in medical imaging
. Amsterdam: Elsevier.
4. Doi, K., MacMahon, H., Katsuragawa, S., Nishikawa, R. M., & Jiang, Y. (1999). Computer-
aided diagnosis in radiology: Potential and pitfalls.
European Journal of Radiology
,
31
, 97-109.
5. Doi, K. (2000). Present status and future horizons for computer aided diagnosis in radiology. In:
P. E. Sharp & A. C. Perkins (Eds.),
Physics and engineering in medicine in the new millennium
(pp. 84-87). London: Institute of Physics and Engineering in Medicine.
