Decoding the Surgical Scene: A Scoping Review of Scene Graphs in Surgery
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| Format: | Preprint |
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2025
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| _version_ | 1866917545499426816 |
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| author | Henriques, Angelo Hoxha, Korab Zapp, Daniel Issa, Peter C. Navab, Nassir Nasseri, M. Ali |
| author_facet | Henriques, Angelo Hoxha, Korab Zapp, Daniel Issa, Peter C. Navab, Nassir Nasseri, M. Ali |
| contents | As surgical AI transitions from pixel-level detection to complex reasoning, Scene Graphs (SGs) offer the structured, relational representations necessary to decode dynamic surgical environments. This PRISMA-ScR-guided scoping review systematically maps the evolving landscape of SG research in surgery, analyzing 52 primary studies to chart applications and methodological shifts. Our analysis reveals rapid growth, yet uncovers a critical 'data divide': internal-view research (e.g., triplet recognition from endoscopic video) accounts for 81% of studies and almost exclusively uses real-world 2D video, while external-view operating room modeling relies heavily on simulated data. Methodologically, we identify a decisive shift from foundational graph neural networks to specialized foundation models and generative AI, which together now account for approximately 50% of research in 2025. Crucially, our synthesis suggests that Scene Graphs are evolving from simple descriptors into essential 'neuro-symbolic guardrails', providing the structured, verifiable intermediate representation needed to prevent hallucinations in increasingly autonomous Surgical Foundation Models. Despite this promise, a major translational gap remains: none of the reviewed studies have proceeded to prospective clinical validation. We conclude that bridging this gap requires moving beyond standard computer vision metrics; we therefore propose the 'Validation Trinity' -- prioritizing Semantic Query Success, Latency-Aware Accuracy, and Safety-Critical Recall -- as the necessary evaluation framework to bring graph-based surgical AI into clinical practice. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_20941 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decoding the Surgical Scene: A Scoping Review of Scene Graphs in Surgery Henriques, Angelo Hoxha, Korab Zapp, Daniel Issa, Peter C. Navab, Nassir Nasseri, M. Ali Computer Vision and Pattern Recognition I.2.10; I.4.8; J.3 As surgical AI transitions from pixel-level detection to complex reasoning, Scene Graphs (SGs) offer the structured, relational representations necessary to decode dynamic surgical environments. This PRISMA-ScR-guided scoping review systematically maps the evolving landscape of SG research in surgery, analyzing 52 primary studies to chart applications and methodological shifts. Our analysis reveals rapid growth, yet uncovers a critical 'data divide': internal-view research (e.g., triplet recognition from endoscopic video) accounts for 81% of studies and almost exclusively uses real-world 2D video, while external-view operating room modeling relies heavily on simulated data. Methodologically, we identify a decisive shift from foundational graph neural networks to specialized foundation models and generative AI, which together now account for approximately 50% of research in 2025. Crucially, our synthesis suggests that Scene Graphs are evolving from simple descriptors into essential 'neuro-symbolic guardrails', providing the structured, verifiable intermediate representation needed to prevent hallucinations in increasingly autonomous Surgical Foundation Models. Despite this promise, a major translational gap remains: none of the reviewed studies have proceeded to prospective clinical validation. We conclude that bridging this gap requires moving beyond standard computer vision metrics; we therefore propose the 'Validation Trinity' -- prioritizing Semantic Query Success, Latency-Aware Accuracy, and Safety-Critical Recall -- as the necessary evaluation framework to bring graph-based surgical AI into clinical practice. |
| title | Decoding the Surgical Scene: A Scoping Review of Scene Graphs in Surgery |
| topic | Computer Vision and Pattern Recognition I.2.10; I.4.8; J.3 |
| url | https://arxiv.org/abs/2509.20941 |