Surgical Data Science -- from Concepts toward Clinical Translation

TL;DR

This paper reviews the current state of Surgical Data Science, highlighting challenges and providing a roadmap for translating data-driven approaches into clinical practice to improve surgical healthcare.

Contribution

It offers a comprehensive overview of SDS practices, standards, and tools, and proposes a strategic roadmap for accelerating clinical translation.

Findings

Identification of key challenges in data acquisition and sharing

Review of existing SDS products and their clinical impact

A proposed roadmap for faster clinical translation of SDS

Abstract

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process.