Combining visual analytics and case-based reasoning for rupture risk assessment of intracranial aneurysms

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. We present an interactive visual exploration tool for the case-based reasoning of IAs. In presence of a new aneurysm 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 scatterplot matrix 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. 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. 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.

[1]  J. Mocco,et al.  MORPHOLOGY PARAMETERS FOR INTRACRANIAL ANEURYSM RUPTURE RISK ASSESSMENT , 2008, Neurosurgery.

[2]  Norman Juchler,et al.  External validation of cerebral aneurysm rupture probability model with data from two patient cohorts , 2018, Acta Neurochirurgica.

[3]  Bernhard Preim,et al.  Semiautomatic neck curve reconstruction for intracranial aneurysm rupture risk assessment based on morphological parameters , 2018, International Journal of Computer Assisted Radiology and Surgery.

[4]  Stephanie M. Bryant,et al.  A case-based reasoning approach to bankruptcy prediction modeling , 1997, Intell. Syst. Account. Finance Manag..

[5]  Baghdad Atmani,et al.  Diabetes Diagnosis by Case-Based Reasoning and Fuzzy Logic , 2018, Int. J. Interact. Multim. Artif. Intell..

[6]  Bernhard Preim,et al.  Rupture Status Classification of Intracranial Aneurysms Using Morphological Parameters , 2018, 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS).

[7]  Agnar Aamodt,et al.  Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches , 1994, AI Commun..

[8]  J. Sayre,et al.  Comparative Morphological Analysis of the Geometry of Ruptured and Unruptured Aneurysms , 2011, Neurosurgery.

[9]  Yuichi Murayama,et al.  Unruptured Intracranial Aneurysms: Incidence of Rupture and Risk Factors , 2009, Stroke.

[10]  Isabelle Bichindaritz,et al.  Medical applications in case-based reasoning , 2005, The Knowledge Engineering Review.

[11]  Norman Juchler,et al.  Extending statistical learning for aneurysm rupture assessment to Finnish and Japanese populations using morphology, hemodynamics, and patient characteristics. , 2019, Neurosurgical focus.

[12]  M. L. Raghavan,et al.  Three-Dimensional Geometrical Characterization of Cerebral Aneurysms , 2004, Annals of Biomedical Engineering.

[13]  Fernando Mut,et al.  Development and internal validation of an aneurysm rupture probability model based on patient characteristics and aneurysm location, morphology, and hemodynamics , 2018, International Journal of Computer Assisted Radiology and Surgery.

[14]  Yi Qian,et al.  Multiple Aneurysms AnaTomy CHallenge 2018 (MATCH)—phase II: rupture risk assessment , 2019, International Journal of Computer Assisted Radiology and Surgery.

[15]  Yutaka Hata,et al.  Computer-Aided Diagnosis of Intracranial Aneurysms in MRA Images with Case-Based Reasoning , 2006, IEICE Trans. Inf. Syst..

[16]  Chun-Ling Chuang,et al.  Case-based reasoning support for liver disease diagnosis , 2011, Artif. Intell. Medicine.

[17]  Cristina Gena,et al.  Methods and techniques for the evaluation of user-adaptive systems , 2005, The Knowledge Engineering Review.

[18]  Juan M. Corchado,et al.  Case-Based Reasoning Applied to Medical Diagnosis and Treatment , 2013, DCAI.