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Toward cognitive pipelines of medical assistance algorithms

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

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.

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Notes

  1. https://www.w3.org/RDF/.

  2. http://www.w3.org/TR/owl2-overview/.

  3. http://www.cognitionguidedsurgery.de.

  4. http://www.rsna.org/radlex.aspx.

  5. http://sig.biostr.washington.edu/projects/fm/.

  6. http://www.xnat.org.

References

  1. Ferrucci D (2010) Build watson: an overview of deepqa for the jeopardy challenge. In: Proceedings of international conference on parallel architecture and compilation techniques, ACM pp 1–2

  2. Butcher L (2012) Case study: how health it is changing the practice of oncology using data in new ways. Oncol Times 34(24):29–30

    Article  Google Scholar 

  3. Gil Y, Gonzlez-Calero PA, Kim J, Moody J, Ratnakar V (2011) A semantic framework for automatic generation of computational workflows using distributed data and component catalogues. J Exp Theor Artif Intell 23(4):389–467

    Article  Google Scholar 

  4. Oinn T, Greenwood M, Addis M, Alpdemir MN, Ferris J, Glover K, Goble C, Goderis A, Hull D, Marvin D, Li P, Lord P, Pocock MR, Senger M, Stevens R, Wipat A, Wroe C (2006) Taverna: lessons in creating a workflow environment for the life sciences. Concurr Comput Pract Exp 18(10):1067–1100

    Article  Google Scholar 

  5. Wood I, Vandervalk B, McCarthy L, Wilkinson M (2012) OWL-DL domain-models as abstract workflows. In: Margaria T, Steffen B (eds) Leveraging applications of formal methods, verification and validation. Applications and case studies. Volume 7610 of lecture notes in computer science. Springer, Berlin, pp 56–66

    Chapter  Google Scholar 

  6. Beygelzimer A, Riabov A, Sow D, Turaga DS, Udrea O (2013) Big data exploration via automated orchestration of analytic workflows. In: Proceedings of international conference on autonomic computing, San Jose, CA, pp 153–158

  7. Brazdil P, Carrier CG, Soares C, Vilalta R (2008) Metalearning: applications to data mining. Springer, Berlin

    Google Scholar 

  8. Nguyen P, Hilario M, Kalousis A (2014) Using meta-mining to support data mining workflow planning and optimization. J Artif Intell Res 51:605–644

  9. Kotthoff L (2014) Algorithm selection for combinatorial search problems: a survey. AI Mag 35(3):48–60

    Google Scholar 

  10. Thornton C, Hutter F, Hoos HH, Leyton-Brown K (2013) Auto-weka: combined selection and hyperparameter optimization of classification algorithms. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, ACM pp 847–855

  11. Samulowitz H, Sabharwal A, Reddy C (2014) Cognitive automation of data science. In: ICML AutoML workshop

  12. Clearly K, Chung HY, Mun SK (2005) Or 2020: the operating room of the future. Laparoendosc Adv Surg Tech 15(5):495–500

    Article  Google Scholar 

  13. Kranzfelder M, Staub C, Fiolka A, Schneider A, Gillen S, Wilhelm D, Friess H, Knoll A, Feussner H (2013) Toward increased autonomy in the surgical OR: needs, requests, and expectations. Surg Endosc 27(5):1681–1688

    Article  PubMed  Google Scholar 

  14. Joyce JP, Lapinsky GW (1983) A history and overview of the safety parameter display system concept. Nucl Sci IEEE Trans 30(1):744–749

    Article  Google Scholar 

  15. Neumuth T, Strau G, Meixensberger J, Lemke H, Burgert O (2006) Acquisition of process descriptions from surgical interventions. Database and expert systems applications. Volume 4080 of lecture notes in computer science. Springer, Berlin, pp 602–611

    Google Scholar 

  16. Lalys F, Bouget D, Riffaud L, Jannin P (2013) Automatic knowledge-based recognition of low-level tasks in ophthalmological procedures. Int J Comput Assist Radiol Surg 8(1):39–49

    Article  PubMed  Google Scholar 

  17. Speidel S, Benzko J, Krappe S, Sudra G, Azad P, Müller-Stich BP, Gutt C, Dillmann R (2009) Automatic classification of minimally invasive instruments based on endoscopic image sequences, vol 7261. In: Proceedings of SPIE

  18. Neumuth T, Jannin P, Strauss G, Meixensberger J, Burgert O (2009) Validation of knowledge acquisition for surgical process models. J Am Med Inf Assoc 16(1):72–80

  19. Katic D, Wekerle AL, Gärtner F, Kenngott HG, Müller-Stich BP, Dillmann R, Speidel S (2014) Knowledge-driven formalization of laparoscopic surgeries for rule-based intraoperative context-aware assistance. In: IPCAI

  20. Gemmeke P, Maleshkova M, Philipp P, Götz M, Weber C, Kämpgen B, Zelzer S, Maier-Hein K, Rettinger A (2014) Using linked data and Web APIs for automating the pre-processing of medical images. COLD (ISWC)

  21. Philipp P, Maleshkova M, Götz M, Weber C, Kämpgen B, Zelzer S, Maier-Hein K, Rettinger A (2015) Automatisierte Verarbeitung von Bildverarbeitungsalgorithmen mit semantischen Technologien. In: BVM

  22. Krötzsch M, Vrandečić D, Völkel M (2006) Semantic mediawiki. In: The Semantic Web-ISWC 2006. Springer, pp 935–942

  23. Speiser S, Harth A (2011) Integrating linked data and services with linked data services. In: The semantic web: research and appl. Springer 170–184

  24. Wolpert DH, Macready WG (1997) No free lunch theorems for optimization. Evol Comput IEEE Trans 1(1):67–82

    Article  Google Scholar 

  25. Freund Y, Schapire RE (1995) A desicion-theoretic generalization of on-line learning and an application to boosting. In: Vitányi P (ed) Computational learning theory. Springer, Heidelberg, pp 23–37

  26. Breiman L (1996) Bagging predictors. Mach learn 24(2):123–140

    Google Scholar 

  27. Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241–259

    Article  Google Scholar 

  28. Jacobs RA, Jordan MI, Nowlan SJ, Hinton GE (1991) Adaptive mixtures of local experts. Neural Comput 3(1):79–87

    Article  Google Scholar 

  29. Stadtmüller S, Speiser S, Harth A, Studer R (2013) Data-fu: a language and an interpreter for interaction with read/write linked data. In: Proceedings of the international conference on World Wide Web, pp 1225–1236

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Acknowledgments

This work was carried out with the support of the German Research Foundation (DFG) within projects I01, A01, R01, S01 and I04, SFB/TRR 125 “Cognition-Guided Surgery”.

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Authors and Affiliations

  1. Institute AIFB, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Patrick Philipp, Maria Maleshkova, Achim Rettinger, Benedikt Kämpgen & Rudi Studer

  2. Institute for Anthropomatics, Karlsruhe Institute of Technology, Karlsruhe, Germany

    Darko Katic, Stefanie Speidel & Rüdiger Dillmann

  3. Klinik für Allgemein-, Viszeral- und Transplantationschirurgie, University of Heidelberg, Heidelberg, Germany

    Anna-Laura Wekerle, Hannes Kenngott & Beat Müller

  4. Division of Medical and Biological Informatics, German Cancer Research Center (DKFZ), Heidelberg, Germany

    Christian Weber, Michael Götz & Marco Nolden

Authors
  1. Patrick Philipp
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  2. Maria Maleshkova
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  3. Darko Katic
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  4. Christian Weber
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  5. Michael Götz
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  6. Achim Rettinger
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  7. Stefanie Speidel
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  8. Benedikt Kämpgen
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  9. Marco Nolden
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  10. Anna-Laura Wekerle
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  11. Rüdiger Dillmann
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  12. Hannes Kenngott
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  13. Beat Müller
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  14. Rudi Studer
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Corresponding author

Correspondence to Patrick Philipp.

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Conflict of interest

Patrick Philipp, Maria Maleshkova, Darko Katic, Christian Weber, Michael Götz, Stefanie Speidel, Achim Rettinger, Benedikt Kämpgen, Marco Nolden, Anna-Laura Wekerle, Rüdiger Dillmann, Hannes Kenngott, Beat Müller and Rudi Studer declare that they have no conflict of interest.

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Philipp, P., Maleshkova, M., Katic, D. et al. Toward cognitive pipelines of medical assistance algorithms. Int J CARS 11, 1743–1753 (2016). https://doi.org/10.1007/s11548-015-1322-y

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  • DOI: https://doi.org/10.1007/s11548-015-1322-y

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