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Surgical Data Science: Enabling Next-Generation Surgery

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arxiv 1701.06482 v2 pith:APH4KXZM submitted 2017-01-23 cs.CY

classification cs.CY
keywords datasciencesurgicalconsensusfieldaccessadvancesadvancing
verification ladder T0 review T1 audit T2 compute T3 formal
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This paper introduces Surgical Data Science as an emerging scientific discipline. Key perspectives are based on discussions during an intensive two-day international interactive workshop that brought together leading researchers working in the related field of computer and robot assisted interventions. Our consensus opinion is that increasing access to large amounts of complex data, at scale, throughout the patient care process, complemented by advances in data science and machine learning techniques, has set the stage for a new generation of analytics that will support decision-making and quality improvement in interventional medicine. In this article, we provide a consensus definition for Surgical Data Science, identify associated challenges and opportunities and provide a roadmap for advancing the field.

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  1. ArthroPhase: A Novel Dataset and Method for Phase Recognition in Arthroscopic Video

    cs.CV 2025-02 conditional novelty 6.0 of 10

    A transformer-based model with a temporal progress head reaches 72.91% accuracy on the new ACL27 arthroscopic phase dataset and 92.4% on Cholec80, establishing an early benchmark for arthroscopy.

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