Multimodal model predicts operator retreat in robotic flexible endoscopy
Multimodal Progress-Residual Fusion for Anticipating Navigation Retreat in Robotic Flexible Endoscopy
Robotics
Summary
Flexible robotic endoscopes can get stuck or meet resistance inside the body because of twisting anatomy and interactions with tissue walls. When this happens, the operator decides to pull back the scope to avoid problems. The authors developed a model that watches the operator’s commands, tracking signals, and video feed to predict when this retreat decision is about to happen. This prediction helps understand the relationship between what the operator tries to do and what actually happens inside the body, potentially improving navigation assistance.
What this means in practice
- •For medical device teams: Detect operator retreat intentions early during robotic endoscopy to assist navigation and improve procedural safety.$Commercial implications: Enables safer robotic endoscopy systems that anticipate operator actions and provide real-time behavioral assistance.
- •For surgical robotics engineers: Integrate multimodal sensor data to improve control algorithms predicting navigation difficulties in flexible endoscopic tools.
Authors
Chi Kit Ng, Tianchun Wu, Jiatan Sun, Yuming Sun, Jianru Zhang, Hongliang Ren
Abstract
Flexible robotic endoscopes provide access through tortuous anatomy, where friction, deformation, and wall interaction separate commanded insertion from useful distal progress. Operators interpret this mismatch and retreat to recover a navigable configuration. Recognizing the approach to that decision requires relating motor intent to distal response and the local visual scene. We introduce Multimodal Progress-Residual Fusion (MPRF), a causal model that anticipates operator retreat from commands, electromagnetic (EM) tracking, and endoscopic video. A nominal-progress model predicts the response expected in normal or successful navigation; a temporal predictor combines its EM residual with all three observed modalities. In a 150-episode colon-phantom collection, MPRF achieves 0.580 +/- 0.007 AUPRC across three seeds, compared with 0.511 for the strongest reduced-modality control. Complete multimodal histories provide the principal ranking advantage, while the residual adds an explicit intent-response representation with a small, uncertain increment over raw fusion. Analysis of frozen alarms relates warning timing to the behavioral label and explains the increase in event recall under a longer matching window. These results establish a basis for behavioral assistance during operator-controlled navigation.