surgical simulation developers

Abstract: The assessment of surgical skills in robotic surgery training has traditionally relied on subjective evaluations by human experts, creating bottlenecks in surgical education and limiting scalability. We present a novel foundation model that automatically analyzes robotic surgical videos to provide comprehensive skill assessment and gesture understanding. Combining a visual encoder with a transformer architecture, the model performs three key tasks simultaneously: 1) surgical task classification (knot tying, needle passing, suturing), 2) skill level assessment on a standardized scale, and 3) natural language generation of surgical gesture descriptions. The model processes video sequences from the da Vinci surgical robot and generates human-readable text descriptions of surgical actions, similar to how language models generate text from prompts, using a multi-task learning framework with adaptive uncertainty weighting to optimize performance across all objectives. Our transformer-based architecture incorporates positional encoding for temporal understanding and attention mechanisms for spatiotemporal feature integration. Evaluated on the da Vinci Skills Simulator dataset, the proposed framework demonstrates state-of-the-art performance in surgical task recognition and provides interpretable skill assessments that correlate with expert evaluations, with the model's ability to generate natural language descriptions of surgical gestures opening new possibilities for automated surgical education, real-time feedback systems, and objective skill assessment in surgical training and certification programs.

Abstract: High-fidelity surgical video generation is increasingly important for surgical education, simulation, and downstream AI training, as conventional simulation pipelines are limited by cost, realism, and scenario diversity. Adapting generic text-to-video diffusion models to surgical procedures remains difficult, however, due to complex anatomical structures, intricate tool-tissue interactions, the inherently hierarchical nature of surgical workflows, and the scarcity of curated training data outside a few well-studied procedures. We propose a surgical video generation framework that combines hierarchical procedural annotation with curriculum transfer learning. Surgical procedures are decomposed into three levels (phase, task, and action) and a pretrained text-to-video diffusion model is progressively adapted from coarse to fine-grained surgical context, supported by a dynamic sampling strategy that handles variable-resolution and variable-length surgical clips. We instantiate the framework on Bilateral Axillo-Breast Approach robotic thyroidectomy, a procedure for which no curated benchmark currently exists. Relative to the preceding task-level model, the final action-level model reduces FVD by 20.1% and FID by 15.7%, and a matched-budget ablation indicates that these gains arise from the curriculum design rather than from additional training. A user study with six clinicians rates visual realism highly while highlighting remaining gaps in anatomical accuracy and clinical reliability. The proposed framework offers a step toward AI-driven surgical simulation in procedures where curated data are scarce.

Abstract: Surgical video generation holds substantial potential for surgical education, simulation, and data augmentation, yet generating surgical videos with realistic and clinically plausible motion remains challenging. Most existing methods rely on auxiliary conditions, such as masks, trajectories, depth, or reference videos, to achieve visually plausible synthesis. Yet, these auxiliary conditions typically require additional manual annotation or specialized acquisition, making it difficult to scale such methods beyond small, curated datasets. This motivates the need for a reference-free architecture capable of generating high-quality surgical video without requiring auxiliary visual conditions at inference time. We propose FLAIR, a Flow-guided LatentAction Injection framework for Reference-free surgical video generation. FLAIR learns action priors from optical flow of real surgical videos, dynamically predicts corresponding latent action representation from an input prompt, and injects it into a frozen base model to generate surgical videos with improved action consistency. We further construct SurgActionClip-30K, the first large-scale surgical vision dataset comprising action-centric segmented clips and structured caption labels, addressing the persistent lack of fine-grained, action-centric surgical datasets. Lastly, we introduce SurgMetrics, the first surgical domain-specific evaluation metrics for quantifying the quality of generated surgical videos, addressing the persistent absence of clinically grounded evaluation standards in this domain. Extensive experiments demonstrate that FLAIR enables generating high-quality surgical videos using text-only inference without auxiliary conditions, and validation in SurgMetrics demonstrates its strength in alignment with human perception compared to traditional metrics.