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Large-scale self-supervised video foundation model for intelligent surgery

Computer-Assisted Intervention has the potential to revolutionize modern surgery, with surgical scene understanding serving as a critical component in supporting decision-making and improving procedural efficacy. While existing AI-driven approaches alleviate annotation burdens via self-supervised sp...

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Autori principali: Yang, Shu (Autore) , Zhou, Fengtao (Autore) , Mayer, Leon (Autore) , Huang, Fuxiang (Autore) , Chen, Yiliang (Autore) , Wang, Yihui (Autore) , He, Sunan (Autore) , Nie, Yuxiang (Autore) , Wang, Xi (Autore) , Sümer, Ömer (Autore) , Jin, Yueming (Autore) , Sun, Huihui (Autore) , Xu, Shuchang (Autore) , Liu, Alex Qinyang (Autore) , Li, Zheng (Autore) , Qin, Jing (Autore) , Teoh, Jeremy YuenChun (Autore) , Maier-Hein, Lena (Autore) , Chen, Hao (Autore)
Natura: Article (Journal)
Lingua:inglese
Pubblicazione: 04 February 2026
In: npj digital medicine
Year: 2026, Volume: 9, Fascicolo: 1, Pages: 1-16
ISSN:2398-6352
DOI:10.1038/s41746-026-02403-0
Accesso online:Resolving-System, kostenfrei, Volltext: https://doi.org/10.1038/s41746-026-02403-0
Verlag, kostenfrei, Volltext: https://www.nature.com/articles/s41746-026-02403-0
Testo
Note sull'autore:Shu Yang, Fengtao Zhou, Leon Mayer, Fuxiang Huang, Yiliang Chen, Yihui Wang, Sunan He, Yuxiang Nie, Xi Wang, Ömer Sümer, Yueming Jin, Huihui Sun, Shuchang Xu, Alex Qinyang Liu, Zheng Li, Jing Qin, Jeremy YuenChun Teoh, Lena Maier-Hein & Hao Chen
Descrizione
Riassunto:Computer-Assisted Intervention has the potential to revolutionize modern surgery, with surgical scene understanding serving as a critical component in supporting decision-making and improving procedural efficacy. While existing AI-driven approaches alleviate annotation burdens via self-supervised spatial representation learning, their lack of explicit temporal modeling during pre-training fundamentally restricts the capture of dynamic surgical contexts, resulting in incomplete spatiotemporal understanding. In this work, we introduce the first video-level surgical pre-training framework that enables joint spatiotemporal representation learning from large-scale surgical video data. To achieve this, we constructed a large-scale surgical video dataset comprising 3650 videos and 3.55 million frames, spanning more than 20 surgical procedures and over 10 anatomical structures. Building upon this dataset, we propose SurgVISTA (Surgical Video-level Spatial-Temporal Architecture), a reconstruction-based pre-training method that jointly captures intricate spatial structures and temporal dynamics. Additionally, SurgVISTA incorporates image-level knowledge distillation guided by a surgery-specific expert model to enhance the learning of fine-grained anatomical and semantic features. To validate its effectiveness, we established a comprehensive benchmark comprising 13 video-level datasets spanning six surgical procedures across four tasks. Extensive experiments demonstrate that SurgVISTA consistently outperforms both natural- and surgical-domain pre-trained models, demonstrating strong potential to advance intelligent surgical systems in clinically meaningful scenarios.
Descrizione del documento:Version of record: 16. März 2026
Gesehen am 25.08.2026
Descrizione fisica:Online Resource
ISSN:2398-6352
DOI:10.1038/s41746-026-02403-0

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