Dataset Information

Surgical data science - from concepts toward clinical translation.

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.

SUBMITTER: Maier-Hein L

PROVIDER: S-EPMC9135051 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Surgical data science - from concepts toward clinical translation.

Maier-Hein Lena L Eisenmann Matthias M Sarikaya Duygu D März Keno K Collins Toby T Malpani Anand A Fallert Johannes J Feussner Hubertus H Giannarou Stamatia S Mascagni Pietro P Nakawala Hirenkumar H Park Adrian A Pugh Carla C Stoyanov Danail D Vedula Swaroop S SS Cleary Kevin K Fichtinger Gabor G Forestier Germain G Gibaud Bernard B Grantcharov Teodor T Hashizume Makoto M Heckmann-Nötzel Doreen D Kenngott Hannes G HG Kikinis Ron R Mündermann Lars L Navab Nassir N Onogur Sinan S Roß Tobias T Sznitman Raphael R Taylor Russell H RH Tizabi Minu D MD Wagner Martin M Hager Gregory D GD Neumuth Thomas T Padoy Nicolas N Collins Justin J Gockel Ines I Goedeke Jan J Hashimoto Daniel A DA Joyeux Luc L Lam Kyle K Leff Daniel R DR Madani Amin A Marcus Hani J HJ Meireles Ozanan O Seitel Alexander A Teber Dogu D Ückert Frank F Müller-Stich Beat P BP Jannin Pierre P Speidel Stefanie S

Medical image analysis 20211118

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 succe ...[more]

PMID: 34879287

Similar Datasets

Project description:Healthcare in general, and surgery/interventional care in particular, is evolving through rapid advances in technology and increasing complexity of care with the goal of maximizing quality and value of care. While innovations in diagnostic and therapeutic technologies have driven past improvements in quality of surgical care, future transformation in care will be enabled by data. Conventional methodologies, such as registry studies, are limited in their scope for discovery and research, extent and complexity of data, breadth of analytic techniques, and translation or integration of research findings into patient care. We foresee the emergence of Surgical/Interventional Data Science (SDS) as a key element to addressing these limitations and creating a sustainable path toward evidence-based improvement of interventional healthcare pathways. SDS will create tools to measure, model and quantify the pathways or processes within the context of patient health states or outcomes, and use information gained to inform healthcare decisions, guidelines, best practices, policy, and training, thereby improving the safety and quality of healthcare and its value. Data is pervasive throughout the surgical care pathway; thus, SDS can impact various aspects of care including prevention, diagnosis, intervention, or post-operative recovery. Existing literature already provides preliminary results suggesting how a data science approach to surgical decision-making could more accurately predict severe complications using complex data from pre-, intra-, and post-operative contexts, how it could support intra-operative decision-making using both existing knowledge and continuous data streams throughout the surgical care pathway, and how it could enable effective collaboration between human care providers and intelligent technologies. In addition, SDS is poised to play a central role in surgical education, for example, through objective assessments, automated virtual coaching, and robot-assisted active learning of surgical skill. However, the potential for transforming surgical care and training through SDS may only be realized through a cultural shift that not only institutionalizes technology to seamlessly capture data but also assimilates individuals with expertise in data science into clinical research teams. Furthermore, collaboration with industry partners from the inception of the discovery process promotes optimal design of data products as well as their efficient translation and commercialization. As surgery continues to evolve through advances in technology that enhance delivery of care, SDS represents a new knowledge domain to engineer surgical care of the future.

Dataset Information

Surgical data science - from concepts toward clinical translation.

Publications

Surgical data science - from concepts toward clinical translation.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets