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Künstliche Intelligenz in der Kinderchirurgie

Gegenwart und Zukunft

Artificial intelligence in pediatric surgery

Present and future

  • Leitthema
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Zusammenfassung

Hintergrund

Künstliche Intelligenz, Automatisierung und digitale Transformation dominieren zunehmend die Prozesse in nahezu allen Branchen. Auch die Medizin und die Medizinindustrie in Deutschland verschließen sich dieser Entwicklung nicht mehr, denn mitunter werden die Vorteile erkannt, die neue Gedankenspiele und neue Prozesse auch in der Medizin, und speziell in einem so überschaubaren Fachgebiet wie der Kinderchirurgie, bieten.

Ziel der Arbeit

Erhebung einer Status-quo-Analyse künstlicher Intelligenz in der internationalen Kinderchirurgie mit Diskussion von Zukunftsperspektiven und Ratschlägen aus Autorensicht.

Material und Methoden

Auswertung und Diskussion internationaler Publikationen, externer Expertenempfehlungen und persönlicher Erfahrungen der Autoren.

Ergebnisse

Die internationalen Erfahrungen mit Anwendungen künstlicher Intelligenz in der Chirurgie sind mittlerweile sehr vielfältig. Viele dieser Entwicklungen können auch kinderchirurgisch genutzt und weiterentwickelt werden. Die Erfahrungen mit speziellen kinderchirurgischen Entwicklungen im Bereich künstlicher Intelligenz beschränken sich jedoch bis dato noch auf Einzelfälle.

Schlussfolgerung

Auch die Kinderchirurgie darf sich dem Trend zu Anwendungen künstlicher Intelligenz im Arbeitsalltag nicht verschließen. Zusätzlich zur Etablierung allgemeiner vorhandener Entwicklungen sollten die speziellen kinderchirurgischen Anforderungen in zukünftigen Forschungsprojekten Beachtung finden. Mitunter hierzu wurde im September 2019 die Arbeitsgemeinschaft „Digitalisierung“ der Deutschen Gesellschaft für Kinderchirurgie gegründet.

Abstract

Background

Artificial intelligence, automatization and digital transformation increasingly dominate the business models of almost all enterprises. Even in medicine and medical technology, companies also no longer close their minds to this development as the advantages provided by the new ideas and processes in medicine and particularly in compact disciplines, such as pediatric surgery have occasionally been recognized.

Objective

This article gives a status quo analysis of artificial intelligence in international pediatric surgery with a discussion of future perspectives and suggestions from the authors’ perspective.

Material and methods

Appraisal and discussion of international publications, external expert opinions and personal experiences of the authors.

Results

A wide spectrum of applications using artificial intelligence in surgery is internationally available. Many of these developments can also be further adapted for use in pediatric surgery. The experience using artificial intelligence for special pediatric surgical indications is currently limited to isolated cases.

Conclusion

Disciplines such as pediatric surgery cannot disregard the trend towards the application of artificial intelligence in daily practice. In addition to the establishment of current developments, the requirements of pediatric surgery should also be taken into account. These were some of the impulses that led to the founding of the working group on digitalization of the German Association for Pediatric Surgery in September 2019.

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Literatur

  1. Deo RC (2015) Machine learning in medicine. Circulation 132(20):1920–1930

    Article  Google Scholar 

  2. Sutton RS, Barto AG (1998) Introduction to reinforcement learning. MIT Press, Cambridge

    Book  Google Scholar 

  3. Soguero-Ruiz C, Fei WM, Jenssen R et al (2015) Data-driven temporal prediction of surgical site infection. AMIA Annu Symp Proc 2015:1164–1173

    PubMed  PubMed Central  Google Scholar 

  4. Bergquist S, Brooks G, Keating N et al (2017) Classifying lung cancer severity with ensemble machine learning in health care claims data. Proc Mach Learn Res 68:25–38

    PubMed  PubMed Central  Google Scholar 

  5. Muensterer OJ, Waldron S, Boo YJ et al (2017) Multiphoton microscopy: a novel diagnostic method for solid tumors in a prospective pediatric oncologic cohort, an experimental study. Int J Surg 48:128–133

    Article  Google Scholar 

  6. Goedeke J, Schreiber P, Seidmann L et al (2019) Multiphoton microscopy in the diagnostic assessment of pediatric solid tissue in comparison to conventional histopathology: results of the first international online interobserver trial. Cancer Manag Res 11:3655–3667

    Article  Google Scholar 

  7. Bera K, Schalper KA, Rimm DL et al (2019) Artificial intelligence in digital pathology—new tools for diagnosis and precision oncology. Nat Rev Clin Oncol. https://doi.org/10.1038/s41571-019-0252-y

    Article  PubMed  PubMed Central  Google Scholar 

  8. Hindelang B, Aguirre J, Schwarz M et al (2019) Non-invasive imaging in dermatology and the unique potential of raster-scan optoacoustic mesoscopy. J Eur Acad Dermatol Venereol 33(6):1051–1061

    Article  CAS  Google Scholar 

  9. Hinton GE, Osindero S, Teh Y‑W (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554

    Article  Google Scholar 

  10. Modifi R, Duff MD, Madhavan KK et al (2007) Identification of severe acute pancreatitis using an artificial neural network. Surgery 141:59–66

    Article  Google Scholar 

  11. Di Russo SM, Chahine AA, Sullivan T et al (2002) Development of a model for prediction of survival in pediatric trauma patients: comparison of artificial neural networks and logistic regression. J Pediatr Surg 37(7):1098–1104

    Article  Google Scholar 

  12. Uemura M, Tomikawa M, Miao T et al (2018) Feasibility of an AI-based measure of the hand motions of expert and novice surgeons. Comput Math Methods Med. https://doi.org/10.1155/2018/9873273

    Article  PubMed  PubMed Central  Google Scholar 

  13. Nadkarni PM, Ohno-Machado L, Chapman WW (2011) Natural language processing: an introduction. J Am Med Inform Assoc 18(5):544–551

    Article  Google Scholar 

  14. Cohen KB, Glass B, Greiner HM et al (2016) Methodological issues in predicting pediatric epilepsy surgery candidates through natural language processing and machine learning. Biomed Inform Insights 8:11–18

    Article  Google Scholar 

  15. Akbilgic O, Homayouni R, Heinrich K et al (2019) Unstructured text in EMR improves prediction of death after surgery in children. Informatics. https://doi.org/10.20944/preprints201810.0678.v1

    Article  Google Scholar 

  16. Veras LV, Arnold M, Avansino JR et al (2019) Guidelines for synoptic reporting of surgery and pathology in Hirschsprung disease. J Pediatr Surg. https://doi.org/10.1016/j.jpedsurg.2019.03.010

    Article  PubMed  Google Scholar 

  17. Behrends W (2019) KI ordnet den medizinischen Befund. https://healthcare-in-europe.com/de/news/ki-ordnet-den-medizinischen-befund.html. Zugegriffen: 27. Sept. 2019

    Google Scholar 

  18. Friedman C, Shagina L, Lussier Y et al (2004) Automated encoding of clinical documents based on natural language processing. J Am Med Inform Assoc 11(5):392–402

    Article  Google Scholar 

  19. Egmont-Petersen M, de Ridder D, Handels H (2002) Image processing with neural networks—a review. Pattern Recognit 35(10):2279–2301

    Article  Google Scholar 

  20. Kenngott HG, Wagner M, Nickel F et al (2015) Computer-assisted abdominal surgery: new technologies. Langenbecks Arch Surg 400(3):273–281

    Article  CAS  Google Scholar 

  21. Bonrath EM, Gordon LE, Grantcharov TP (2015) Characterising “near miss” events in complex laparoscopic surgery through video analysis. BMJ Qual Saf 24(8):516–521

    Article  Google Scholar 

  22. Grenda TR, Pradarelli JC, Dimick JB (2016) Using surgical video to improve technique and skill. Ann Surg 264(1):32–33

    Article  Google Scholar 

  23. Sikka K, Ahmed AA, Diaz D et al (2015) Automated assessment of children’s postoperative pain using computer vision. Pediatr Electron Pages 136(1):e124–e131

    Google Scholar 

  24. Groves P, Kayyali B, Knott D et al (2016) The “big data” revolution in healthcare: accelerating value and innovation. https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-big-data-revolution-in-us-health-care. Zugegriffen: 27. Sept. 2019

    Google Scholar 

  25. Austin PC, Tu JV, Lee DS (2010) Logistic regression had superior performance compared with regression trees for predicting in-hospital mortality in patients hospitalized with heart failure. J Clin Epidemiol 63(10):1145–1155

    Article  Google Scholar 

  26. Bellman RE (2015) Adaptive control processes: a guided tour. Princeton University Press, Princeton

    Google Scholar 

  27. Hopewell S, Loudon K, Clarke MJ et al (2009) Publication bias in clinical trials due to statistical significance or direction of trial results. Cochrane Database Syst Rev 1:MR6

    Google Scholar 

  28. Sussillo D, Barak O (2013) Opening the black box: low-dimensional dynamics in high-dimensional recurrent neural networks. Neural Comput 25(3):626–649

    Article  Google Scholar 

  29. Cabitza F, Rasoini R, Gensini GF (2017) Unintended consequences of machine learning in medicine. JAMA 318(6):517–518

    Article  Google Scholar 

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Authors and Affiliations

  1. Klinik und Poliklinik für Kinderchirurgie, Universitätsmedizin Mainz, Langenbeckstr. 1, 55131, Mainz, Deutschland

    Jan Gödeke FEBPS,  Oliver Muensterer & S. Rohleder

Authors
  1. Jan Gödeke FEBPS
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  2. Oliver Muensterer
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  3. S. Rohleder
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Corresponding author

Correspondence to Jan Gödeke FEBPS.

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Interessenkonflikt

J. Gödeke, O. Muensterer und S. Rohleder geben an, dass kein Interessenkonflikt besteht.

Für diesen Beitrag wurden von den Autoren keine Studien an Menschen oder Tieren durchgeführt. Für die aufgeführten Studien gelten die jeweils dort angegebenen ethischen Richtlinien.

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Gödeke, J., Muensterer, O. & Rohleder, S. Künstliche Intelligenz in der Kinderchirurgie. Chirurg 91, 222–228 (2020). https://doi.org/10.1007/s00104-019-01051-3

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  • DOI: https://doi.org/10.1007/s00104-019-01051-3

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