Surgical Data Science -- from Concepts toward Clinical Translation (2011.02284v2)

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.

• The paper introduces SDS as a transformative field that leverages machine learning to modernize surgical practices.
• It details current challenges in technical infrastructure and data annotation that hinder reliable clinical translation in surgery.
• The study proposes a strategic roadmap for interdisciplinary collaboration to overcome regulatory and operational barriers.

Surgical Data Science: From Concepts toward Clinical Translation

The paper "Surgical Data Science – from Concepts toward Clinical Translation" delineates the emergence and evolution of Surgical Data Science (SDS), earmarking it as a burgeoning field that promises to enhance interventional healthcare by employing data-driven methodologies. It underscores the transformative power of data science and machine learning in reimagining surgical practices traditionally reliant on individual physician expertise.

Overview and Motivation

The authors address a perceptible shortfall in the clinical translation of data-driven approaches within surgery, as compared to other realms like radiology. This disparity catalyzed discussions at a 2019 international workshop, leading to an assessment of current practices, achievements, and deficiencies in SDS. The paper subsequently outlines a roadmap to foster advances in the field. The pragmatic definition of SDS, encompassing the capture, organization, analysis, and modeling of surgical data, underscores its potential to induct precision in surgical interventions.

Key Elements and Current Practice

The narrative dissects the current landscape through three lenses: infrastructure for data acquisition, annotation, and analytics.

1. Technical Infrastructure: There is an unmet need for robust infrastructure supporting the systematic acquisition and storage of surgical data while grappling with existing regulatory constraints. This includes challenges related to data digitization, storage capacity, and interoperability.
2. Data Annotation and Sharing: The scarcity of high-quality annotated data is identified as a bottleneck, hampering the deployment of reliable machine learning models for surgical applications. The diversity in surgical data necessitates standardized ontologies and protocols for consistent data labeling.
3. Data Analytics: SDS carries the transformative promise of reshaping decision-making through AI-driven insights. Yet, the translation of surgical data into valuable clinical insights remains tentative, with a limited number of use cases progressing to clinical trials. The paper underscores both technical and clinical challenges such as the variability of surgical procedures, the need for algorithm robustness, and regulatory hurdles.

Numerical Results and Claims

The paper systematically reviews existing public datasets' limitations, emphasizing the pressing demand for large, representative, multi-center data repositories that can facilitate rigorous validation of SDS methodologies. Notably, the paper presents results from a Delphi process, identifying and ranking strategic goals necessary for propelling SDS forward.

Implications and Future Directions

The authors present a strategic vision premised on collaboration across stakeholders and international working groups to standardize and promote SDS. The juxtaposition of technical and clinical advancements envisions a future where data science meshes seamlessly with surgical practice to engender better patient outcomes and operational efficiency. Importantly, the paper proposes an interdisciplinary approach to training and career development, aimed at nurturing expertise in SDS to drive its practical application forward.

Conclusion

In synthesizing these themes, the paper serves as an impetus for galvanizing efforts around clinical translation of SDS. While acknowledging the strides made in understanding and leveraging data science in surgical contexts, it delineates critical pathways and strategies to mitigate existing impediments. As such, the research contributes foundational insights and a scholarly framework to follow in the evolution of SDS from a conceptual apex to real-world clinical impact.