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, Uli Beuing, Oliver Neyazi, Belal Sandalcioglu, I. Erol Preim, Bernhard Saalfeld, Sylvia |
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 |
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) |
Files in This Item:
File | Description | Size | Format | |
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Spitz et al._Combining_2020.pdf | Zweitveröffentlichung | 3.37 MB | Adobe PDF | View/Open |