Toward cognitive pipelines of medical assistance algorithms
Publication date
2016-09-01
Document type
Research article
Author
Philipp, Patrick
Katic, Darko
Weber, Christian
Götz, Michael
Rettinger, Achim
Speidel, Stefanie
Kämpgen, Benedikt
Nolden, Marco
Wekerle, Anna Laura
Dillmann, Rüdiger
Kenngott, Hannes
Müller, Beat
Studer, Rudi
Organisational unit
Karlsruhe Institute of Technology
Scopus ID
Pubmed ID
ISSN
E-ISSN
Series or journal
International Journal of Computer Assisted Radiology and Surgery
Periodical volume
2016
Periodical issue
11
First page
1743
Last page
1753
Peer-reviewed
✅
Part of the university bibliography
Nein
Keyword
Cognitive architecture
Computer aided medicine
Phase recognition
Semantic Web
Tumor progression mapping
Abstract
Purpose: Assistance algorithms for medical tasks have great potential to support physicians with their daily work. However, medicine is also one of the most demanding domains for computer-based support systems, since medical assistance tasks are complex and the practical experience of the physician is crucial. Recent developments in the area of cognitive computing appear to be well suited to tackle medicine as an application domain. Methods: We propose a system based on the idea of cognitive computing and consisting of auto-configurable medical assistance algorithms and their self-adapting combination. The system enables automatic execution of new algorithms, given they are made available as Medical Cognitive Apps and are registered in a central semantic repository. Learning components can be added to the system to optimize the results in the cases when numerous Medical Cognitive Apps are available for the same task. Our prototypical implementation is applied to the areas of surgical phase recognition based on sensor data and image progressing for tumor progression mappings. Results: Our results suggest that such assistance algorithms can be automatically configured in execution pipelines, candidate results can be automatically scored and combined, and the system can learn from experience. Furthermore, our evaluation shows that the Medical Cognitive Apps are providing the correct results as they did for local execution and run in a reasonable amount of time. Conclusion: The proposed solution is applicable to a variety of medical use cases and effectively supports the automated and self-adaptive configuration of cognitive pipelines based on medical interpretation algorithms.
Version
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