Authors:
Paolo Soda
and
Giulio Iannello
Affiliation:
Università Campus Bio-Medico di Roma, Italy
Keyword(s):
Computer Aided Diagnosis (CAD), Medical Imaging, Indirect ImmunoFluorescence (IIF), One-per-class Classification.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Expert Systems
;
Health Information Systems
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Support for Clinical Decision-Making
;
Symbolic Systems
Abstract:
At the present, Indirect ImmunoFluorescence (IIF) imaging is the recommended method for the detection of antinuclear autoantibodies. IIF diagnosis requires to estimate the fluorescence intensity and to describe the staining pattern, but resources and adequately trained personnel are not always available. In this respect, an evident medical demand is the development of Computer Aided Diagnosis (CAD) tools that can offer a support to physician decision. In this paper we present a comprehensive system that supports the two sides of IIF tests classification. It is based on a cascade of two systems: the first labels the fluorescence intensity, whereas the second recognizes the staining pattern of positive wells. The analysis of its perspective performance shows the system potential in lowering the method variability, in increasing the level of standardization and in reducing the specialist workload by more than 80%.