The importance of machine learning in autonomous actions for surgical decision making

Publikation: Beitrag in FachzeitschriftÜbersichtsartikel (Review)Beigetragen

Beitragende

Abstract

Surgery faces a paradigm shift since it has developed rapidly in recent decades, becoming a high-tech discipline. Increasingly powerful technological developments such as modern operating rooms, featuring digital and interconnected equipment and novel imaging as well as robotic procedures, provide several data sources resulting in a huge potential to improve patient therapy and surgical outcome by means of Surgical Data Science. The emerging field of Surgical Data Science aims to improve the quality of surgery through acquisition, organization, analysis, and modeling of data, in particular using machine learning (ML). An integral part of surgical data science is to analyze the available data along the surgical treatment path and provide a context-aware autonomous action by means of ML methods. Autonomous actions related to surgical decision-making include preoperative decision support, intraoperative assistance functions, as well as robot-assisted actions. The goal is to democratize surgical skills and enhance the collaboration between surgeons and cyber-physical systems by quantifying surgical experience and making it accessible to machines, thereby improving patient therapy and outcome. The article introduces basic ML concepts as enablers for autonomous actions in surgery, highlighting examples for such actions along the surgical treatment path.

Details

OriginalspracheEnglisch
Seiten (von - bis)64–79
Seitenumfang16
FachzeitschriftArtificial Intelligence Surgery
Jahrgang2
Ausgabenummer2
PublikationsstatusVeröffentlicht - 18 Apr. 2022
Peer-Review-StatusNein

Externe IDs

Mendeley 7ec3f6be-eb06-394a-bc83-4d601a59aace
unpaywall 10.20517/ais.2022.02
ORCID /0000-0003-2265-4809/work/149798319
ORCID /0000-0002-4590-1908/work/163293962

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