Evidence map›Paper›PMID 41989518›Full record

ArticleInternational journal of computer assisted radiology and surgery2026

TwinOR: photorealistic digital twins of dynamic operating rooms for embodied AI research.

Han Zhang, Yiqing Shen, Roger D Soberanis-Mukul, Ankita Ghosh, Hao Ding, Lalithkumar Seenivasan, Jose L Porras, Zhekai Mao, Chenjia Li, Wenjie Xiao and 4 more

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Article in International journal of computer assisted radiology and surgery, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Han ZhangJohns Hopkins University, Baltimore, MD, USA. hzhan206@jhu.edu.
Yiqing ShenJohns Hopkins University, Baltimore, MD, USA.
Roger D Soberanis-MukulJohns Hopkins University, Baltimore, MD, USA.
Ankita GhoshJohns Hopkins University, Baltimore, MD, USA.
Hao DingJohns Hopkins University, Baltimore, MD, USA.
Lalithkumar SeenivasanJohns Hopkins University, Baltimore, MD, USA.
Jose L PorrasJohns Hopkins University, Baltimore, MD, USA.
Zhekai MaoJohns Hopkins University, Baltimore, MD, USA.
Chenjia LiJohns Hopkins University, Baltimore, MD, USA.
Wenjie XiaoJohns Hopkins University, Baltimore, MD, USA.
Lonny YarmusJohns Hopkins Medical Institutions, Baltimore, MD, USA.
Angela Christine ArgentoJohns Hopkins Medical Institutions, Baltimore, MD, USA.
Masaru IshiiJohns Hopkins Medical Institutions, Baltimore, MD, USA.
Mathias UnberathJohns Hopkins University, Baltimore, MD, USA. unberath@jhu.edu.

Funding

National Science Foundation 2239077
6 · The paper itself

Abstract

purposeDeveloping embodied AI for intelligent surgical systems requires safe, controllable environments for continual learning and evaluation. However, safety regulations and operational constraints in operating rooms (ORs) limit agents from freely perceiving and interacting in realistic settings. Digital twins provide high-fidelity, risk-free environments for exploration and training. How we may create photorealistic and dynamic digital representations of ORs that capture relevant spatial, visual, and behavioral complexity remains an open challenge.

methodsWe introduce TWINOR, a real-to-sim infrastructure for constructing photorealistic and dynamic digital twins of ORs for embodied AI research. The system reconstructs static geometry from pre-scan videos and continuously models human and equipment motion through multi-view perception of OR activities. The static and dynamic components are fused into an immersive 3D environment that supports controllable simulation and facilitates future embodied exploration.

resultsThe proposed framework reconstructs complete OR geometry with centimeter-level accuracy while preserving dynamic interaction across surgical workflows, enabling realistic renderings and a virtual playground for embodied perception benchmarks. In our experiments, TWINOR synthesizes stereo and monocular RGB streams as well as depth observations for geometry understanding and visual localization tasks. Models such as FoundationStereo and ORB-SLAM3 evaluated on TWINOR-synthesized data achieve performance within their reported accuracy ranges on real-world indoor datasets, demonstrating that TWINOR provides sensor-level realism sufficient for emulating real-world perception and localization challenges in dynamic OR scenes.

conclusionBy establishing a perception-grounded real-to-sim pipeline, TWINOR enables the automatic construction of dynamic, photorealistic digital twins of ORs. As a safe and scalable environment for experimentation and benchmarking, TWINOR opens new opportunities for translating embodied intelligence from simulation to real-world clinical environments, and sets the stage for future research on interaction, autonomy, and human-robot collaboration in the OR.

Indexed as

Artificial IntelligenceComputer SimulationImaging, Three-DimensionalOperating RoomsSurgery, Computer-AssistedHumansIntelligent Systems3D reconstructionDynamic modelingEmbodied perceptionReal-to-simSurgical simulation

Identifiers

PMID41989518

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.