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[Model-based treatment in surgery].

T Vogel1, N Kohn, D Ostler

  • 1Klinik und Poliklinik für Chirurgie, Klinikum rechts der Isar der Technischen Universität München, Ismaninger Str. 22, 81675, München, Deutschland. thomas.j.vogel@tum.de.

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Summary
This summary is machine-generated.

Digitalization in medicine, particularly "Surgery 4.0," aims to improve patient outcomes and reduce costs through advanced diagnostics and treatment. Process modeling and machine learning are key to achieving these surgical advancements.

Keywords:
DigitalizationModelingPatient modelTreatment modelTreatment planning

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Area of Science:

  • Medical technology
  • Surgical innovation

Background:

  • The "magic triangle" in surgery seeks gentler treatments, cost reduction, and improved results.
  • Digitalization offers a path towards "Surgery 4.0" by creating supportive cognitive and collaborative environments.

Purpose of the Study:

  • To explore the role of digitalization in achieving the goals of modern surgery.
  • To establish a framework for cognitive, collaborative diagnostics and treatment in surgical settings.

Main Methods:

  • Process modeling, distinguishing between patient and treatment models, is central to modern treatment planning.
  • Detailed modeling of surgical procedures, down to individual steps, is achievable.
  • Neural networks facilitate knowledge acquisition, machine learning, and adaptive responses in complex surgical processes.

Main Results:

  • Digitalization enables precise data acquisition for academic surgery.
  • Advanced modeling allows for the mapping and reproduction of surgical procedures with high detail.
  • Machine learning offers flexible adaptation to complex scenarios.

Conclusions:

  • Digitalization is essential for the future of surgery.
  • Process modeling is a critical and necessary component of digital surgical advancements.
  • Digital tools enhance surgeon support and data precision in academic surgery.