Modellgestützte Therapie in der Chirurgie View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-02-13

AUTHORS

T. Vogel, N. Kohn, D. Ostler, N. Marahrens, N. Samm, A. Jell, M. Kranzfelder, D. Wilhelm, H. Friess, H. Feußner

ABSTRACT

BACKGROUND: The "magic triangle" in surgery and other disciplines consists of the demand for increasingly gentler forms of treatment, simultaneous cost reduction and the fundamental primacy of improving the quality of results. The digitalization of medicine offers a promising opportunity to do justice to this, also in the sense of "Surgery 4.0". The aim is to create a cognitive, collaborative diagnostics and treatment environment to support the surgeon. METHODS: In the sense of a "theory building" for analysis and planning, process modeling is the cornerstone for modern treatment planning. The main distinction is made between the patient model and the treatment model. The course of the actual surgical treatment can also be modeled: in principle it is possible to describe the course of an operation in such fine detail that the surgical procedure can be mapped and reproduced down to each single step, such as a single implementation of forceps. Basically, this has already been achieved. So-called neural networks also open up completely new forms of knowledge acquisition, machine learning and flexible reaction to nearly all conceivable possibilities in highly complex processes. CONCLUSION: "Digitalization" is a necessary development in surgery. It offers not only countless possibilities to support the surgeon in the field of activity but also the chance of more precise data acquisition with respect to academic surgery. Modeling is an indispensable part of this and must be rigorously implemented and further developed. More... »

PAGES

1-8

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00104-019-0815-6

DOI

http://dx.doi.org/10.1007/s00104-019-0815-6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112096274

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30758596


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