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Updated: Oct 10, 2025
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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
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Surgical data science - from concepts toward clinical translation.
Lena Maier-Hein1, Matthias Eisenmann2, Duygu Sarikaya3
1Division of Computer Assisted Medical Interventions (CAMI), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Mathematics and Computer Science, Heidelberg University, Heidelberg, Germany; Medical Faculty, Heidelberg University, Heidelberg, Germany.
Medical Image Analysis
|December 8, 2021
Summary
Surgical Data Science (SDS) uses machine learning to enhance healthcare quality. This review identifies challenges and proposes a roadmap for faster clinical translation of SDS innovations.
Area of Science:
- Data Science
- Machine Learning
- Surgical Data Science (SDS)
Background:
- Machine learning and data science are transforming surgery.
- Surgical Data Science (SDS) aims to improve interventional healthcare through data.
- Translational success in surgical data science lags behind radiology and clinical data science.
Purpose of the Study:
- To identify reasons for the lack of translational success in SDS.
- To provide a roadmap for advancing the field of SDS.
- To review current practices, achievements, and tools in SDS.
Main Methods:
- International workshop with leading SDS researchers.
- Review of infrastructure, data annotation, sharing, and analytics.
- Analysis of SDS products and academic translational progress.
- International multi-round Delphi process for roadmap development.
Main Results:
- Identified challenges in data acquisition, storage, access, annotation, sharing, and analytics.
- Reviewed current SDS products and academic translational progress.
- Developed a roadmap for accelerating clinical translation of SDS.
Conclusions:
- Addressing infrastructure, data management, and analytics are crucial for SDS.
- Bridging the gap between academic research and clinical application is essential.
- A strategic roadmap is needed to realize the full potential of SDS in improving surgical care.
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