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Titel: Combining visual analytics and case-based reasoning for rupture risk assessment of intracranial aneurysms
Autor(en): Spitz, Lena
Niemann, UliIn der Gemeinsamen Normdatei der DNB nachschlagen
Beuing, OliverIn der Gemeinsamen Normdatei der DNB nachschlagen
Neyazi, BelalIn der Gemeinsamen Normdatei der DNB nachschlagen
Sandalcioglu, I. ErolIn der Gemeinsamen Normdatei der DNB nachschlagen
Preim, BernhardIn der Gemeinsamen Normdatei der DNB nachschlagen
Saalfeld, SylviaIn der Gemeinsamen Normdatei der DNB nachschlagen
Erscheinungsdatum: 2020
Art: Artikel
Sprache: Englisch
URN: urn:nbn:de:gbv:ma9:1-1981185920-823409
Schlagwörter: Visual analytics
Case-based reasoning
Intracranial aneurysms
Rupture risk assessment
Zusammenfassung: 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-Publikation
Nutzungslizenz: (CC BY 4.0) Creative Commons Namensnennung 4.0 International(CC BY 4.0) Creative Commons Namensnennung 4.0 International
Sponsor/Geldgeber: Projekt DEAL 2020
Journal Titel: International journal of computer assisted radiology and surgery
Verlag: Springer
Verlagsort: Berlin
Band: 15
Heft: 9
Originalveröffentlichung: 10.1007/s11548-020-02217-9
Seitenanfang: 1525
Seitenende: 1535
Enthalten in den Sammlungen:Fakultät für Informatik (OA)

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