Feasibility analysis of a case-based reasoning system for automated detection of coronary heart disease from myocardial scintigrams

Myocardial perfusion scintigraphy is a noninvasive diagnostic method for the evaluation of patients with suspected or proven coronary artery disease (CAD). We utilized case-based reasoning (CBR) methods to develop the computer-based image interpretation system SCINA which automatically derives from...

Full description

Saved in:
Bibliographic Details
Published in:Artificial intelligence in medicine 1997, Vol.9 (1), p.61-78
Main Authors: Haddad, Mojgan, Adlassnig, Klaus-Peter, Porenta, Gerold
Format: Article
Language:English
Subjects:
Adaptation, Physiological
Artificial Intelligence
Automated image interpretation
Case based reasoning
Computer applications
Coronary Angiography
Coronary artery disease
Coronary Disease - diagnostic imaging
Coronary Disease - physiopathology
Diagnosis, Computer-Assisted
Diagnostic systems
Heart - diagnostic imaging
Heart - physiopathology
Humans
Image Interpretation, Computer-Assisted
Medicine
Myocardial perfusion scintigraphy
Radionuclide Imaging
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Myocardial perfusion scintigraphy is a noninvasive diagnostic method for the evaluation of patients with suspected or proven coronary artery disease (CAD). We utilized case-based reasoning (CBR) methods to develop the computer-based image interpretation system SCINA which automatically derives from a scintigraphic image data set an assessment concerning the presence of CAD. We compiled a case library of 100 patients who underwent both perfusion scintigraphy and coronary angiography to document or exclude the presence of CAD. The angiographie diagnosis of the retrieved nearest neighbor match of a scintigraphic input case was selected as the CBR diagnosis. We examined the effects of input data granularity, case indexing, similarity metric, and adaptation on the diagnostic accuracy of the CBR application SCINA. For the final prototype, sensitivity and specificity for detection of coronary heart disease were 98% and 70% suggesting that CBR systems may achieve a diagnostic accuracy that appears feasible for clinical use.
ISSN:0933-3657
1873-2860
DOI:10.1016/S0933-3657(96)00361-2