Please use this identifier to cite or link to this item: http://dx.doi.org/10.25673/80386
Title: Combining visual analytics and case-based reasoning for rupture risk assessment of intracranial aneurysms
Author(s): Spitz, Lena
Niemann, UliLook up in the Integrated Authority File of the German National Library
Beuing, OliverLook up in the Integrated Authority File of the German National Library
Neyazi, BelalLook up in the Integrated Authority File of the German National Library
Sandalcioglu, I. ErolLook up in the Integrated Authority File of the German National Library
Preim, BernhardLook up in the Integrated Authority File of the German National Library
Saalfeld, SylviaLook up in the Integrated Authority File of the German National Library
Issue Date: 2020
Type: Article
Language: English
URN: urn:nbn:de:gbv:ma9:1-1981185920-823409
Subjects: Visual analytics
Case-based reasoning
Intracranial aneurysms
Rupture risk assessment
Abstract: Purpose Medical case-based reasoning solves problems by applying experience gained from the outcome of previous treatments of the same kind. Particularly for complex treatment decisions, for example, incidentally found intracranial aneurysms (IAs), it can support the medical expert. IAs bear the risk of rupture and may lead to subarachnoidal hemorrhages. Treatment needs to be considered carefully, since it may entail unnecessary complications for IAs with low rupture risk. With a rupture risk prediction based on previous cases, the treatment decision can be supported. Methods We present an interactive visual exploration tool for the case-based reasoning of IAs. In presence of a newaneurysm of interest, our application provides visual analytics techniques to identify the most similar cases with respect to morphology. The clinical expert can obtain the treatment, including the treatment outcome, for these cases and transfer it to the aneurysm of interest.Our application comprises a heatmap visualization, an adapted scatterplotmatrix and fully or partially directed graphs with a circle- or force-directed layout to guide the interactive selection process. To fit the demands of clinical applications, we further integrated an interactive identification of outlier cases as well as an interactive attribute selection for the similarity calculation. A questionnaire evaluation with six trained physicians was used. Result Our application allows for case-based reasoning of IAs based on a reference data set. Three classifiers summarize the rupture state of the most similar cases. Medical experts positively evaluated the application. Conclusion Our case-based reasoning application combined with visual analytic techniques allows for representation of similar IAs to support the clinician. The graphical representation was rated very useful and provides visual information of the similarity of the k most similar cases.
URI: https://opendata.uni-halle.de//handle/1981185920/82340
http://dx.doi.org/10.25673/80386
Open Access: Open access publication
License: (CC BY 4.0) Creative Commons Attribution 4.0(CC BY 4.0) Creative Commons Attribution 4.0
Sponsor/Funder: Projekt DEAL 2020
Journal Title: International journal of computer assisted radiology and surgery
Publisher: Springer
Publisher Place: Berlin
Volume: 15
Issue: 9
Original Publication: 10.1007/s11548-020-02217-9
Page Start: 1525
Page End: 1535
Appears in Collections:Fakultät für Informatik (OA)

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