# Towards Suturing World Models: Learning Predictive Models for Robotic Surgical Tasks

arXiv: https://arxiv.org/abs/2503.12531 · HTML: https://mlsanity.com/paper/2503.12531/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Medical and surgical world models, one of 11 directions in World models.
- Builds on 11 of its 16 references that are in the map.
- First posted on arXiv 16 Mar 2025.

## Placed in

- World models / [Medical and surgical world models](https://mlsanity.com/wm/medical-and-surgical-world-models/)
- Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/)

## Builds on (most cited first, 11 of 11)

- [HunyuanVideo: A Systematic Framework For Large Video Generative Models](https://mlsanity.com/paper/2412.03603/) (2024)
- [LTX-Video: Realtime Video Latent Diffusion](https://mlsanity.com/paper/2501.00103/) (2024)
- [Endora: Video Generation Models as Endoscopy Simulators](https://mlsanity.com/paper/2403.11050/) (2024)
- [Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects](https://mlsanity.com/paper/2312.10008/) (2023)
- [Surgical Robot Transformer (SRT): Imitation Learning for Surgical Tasks](https://mlsanity.com/paper/2407.12998/) (2024)
- [Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling](https://mlsanity.com/paper/2411.18664/) (2024)
- [SurgSora: Object-Aware Diffusion Model for Controllable Surgical Video Generation](https://mlsanity.com/paper/2412.14018/) (2024)
- [World Models for General Surgical Grasping](https://mlsanity.com/paper/2405.17940/) (2024)
- [Data Augmentation for Surgical Scene Segmentation with Anatomy-Aware Diffusion Models](https://mlsanity.com/paper/2410.07753/) (2024)
- [SimGen: A Diffusion-Based Framework for Simultaneous Surgical Image and Segmentation Mask Generation](https://mlsanity.com/paper/2501.09008/) (2025)
- [Diffusion Stabilizer Policy for Automated Surgical Robot Manipulations](https://mlsanity.com/paper/2503.01252/) (2025)

## Built on by (most cited first, 5 of 5)

- [Cosmos-H-Surgical: Learning Surgical Robot Policies from Videos via World Modeling](https://mlsanity.com/paper/2512.23162/) (2025)
- [From Articulated Kinematics to Routed Visual Control for Action-Conditioned Surgical Video Generation](https://mlsanity.com/paper/2605.08712/) (2026)
- [Medical world models: representing medical states, modelling clinical dynamics and guiding intervention policies](https://mlsanity.com/paper/2606.16721/) (2026)
- [Failure Detection for Surgical Robot Imitation Policies via Flow-Matching World Modeling](https://mlsanity.com/paper/2607.27511/) (2026)
- [SurgVista: Long-Horizon Surgical World Modeling with Plausible Instrument-Tissue Dynamics](https://mlsanity.com/paper/2606.19889/) (2026)

## Abstract (arXiv)

We introduce specialized diffusion-based generative models that capture the spatiotemporal dynamics of fine-grained robotic surgical sub-stitch actions through supervised learning on annotated laparoscopic surgery footage. The proposed models form a foundation for data-driven world models capable of simulating the biomechanical interactions and procedural dynamics of surgical suturing with high temporal fidelity. Annotating a dataset of ~2K clips extracted from simulation videos, we categorize surgical actions into fine-grained sub-stitch classes including ideal and non-ideal executions of needle positioning, targeting, driving, and withdrawal. We fine-tune two state-of-the-art video diffusion models, LTX-Video and HunyuanVideo, to generate high-fidelity surgical action sequences at ≥768x512 resolution and ≥49 frames. For training our models, we explore both Low-Rank Adaptation (LoRA) and full-model fine-tuning approaches. Our experimental results demonstrate that these world models can effectively capture the dynamics of suturing, potentially enabling improved training simulators, surgical skill assessment tools, and autonomous surgical systems. The models also display the capability to differentiate between ideal and non-ideal technique execution, providing a foundation for building surgical training and evaluation systems. We release our models for testing and as a foundation for future research. Project Page: https://mkturkcan.github.io/suturingmodels/