From: AAAI Technical Report WS-93-01. Compilation copyright © 1993, AAAI (www.aaai.org). All rights reserved.
Case-Based
Selection
of Diagnostic
Imaging
Procedures
Charles E. Kahn, Jr.
Departmentof Radiology
Medical College of Wisconsin
Milwaukee, Wisconsin 53226 USA
ckahn@its.mcw.edu
Selecting the most appropriate diagnostic imaging procedure is a complextask: physicians must weigh
the cost, performanceand risks of each procedure. ISIS (Intelligent Selection of ImagingStudies) is
developmentalcase-based decision-support system that helps physicians select diagnostic imagingstudies.
Thesystem encompassesboth episodic and prototypic cases in the domainof radiological proceduressuch as
computedtomography(CT), ultrasound, magnetic resonance imaging (MRI)and angiography.
Eachcase includes the procedurerequested, the clinical informationprovidedand the procedureactually
performed.Eachcase also includes clinical questions to be asked of the referring physician to determinethe
appropriateness of the requested imagingprocedureand to assist the radiologist in formulatinga diagnosis.
The system contains both successful cases and "failures," such as cases with complications or where the
procedure performed was deemedto be inappropriate. There are usually several (if not many)valid
indications for each imagingprocedure, and there are manyfactors that can lead one to modifythe imaging
protocol or to select an alternative procedure.Thesophistication of the systemwill comefromits breadth of
knowledgeabout indications and contra-indications for various procedures.
Thecase base has a distributed representation, and the features indexedallow the system to explain its
reasoning. Indexedfeatures are organizedhierarchically to allow conceptualgeneralization. At present, the
index is maintained manually. Ultimately this process will becomeautomatedor semi-automated,in which
the systemwill create generalizations inductively, or will retrieve old cases and promptthe expert user to
identify the differences betweenthe current case and relevant prior cases to understandthe exceptionsor fine
details of the generalized knowledge.The primarygoals of the system’s knowledgeacquisition process are
to incorporate the valid indications for imagingproceduresand to identify the salient clinical information
necessaryto interpret them.
In retrieving cases, the system gives preference to prototypic cases, becausethese cases represent the
compositeexperienceof several learned cases. Wherea retrieved episodic case presents a serious conflict for
the proposedplan, that case, too, will be retrieved to use in adaptationof the imagingplan. The"statistics"
slot of each case contains informationto indicate the type of case (episode vs. prototype). If the case is
prototype, the numberof cases whichcomprisethat case will be encodedin the statistics field to indicate the
"weightingfactor" of the case’s importance.
Currently, the systemincludes cases relative to only one imagingprocedure, abdominalultrasound. After
validating the system’s indexing and retrieval mechanisms
in this limited domain,wewill present up to 500
requests for imagingproceduresas cases to ISIS. This large numberof cases will providea sufficiently rich
memoryto allow non-trivial reasoning. The system will then be ready for thorough validation of its
knowledge
and reasoning,and for clinical trial in comparisonto physiciansat various levels of expertise. It is
this project’s ultimate goal to embedISIS within the clinical functions of our institution’s radiology
information system.
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