A Recurrent Convolutional Neural Network Approach for Sensorless Force Estimation in Robotic Surgery

TL;DR

This paper presents a neural network model combining CNNs and LSTMs to estimate interaction forces in robotic surgery using video and tool data, addressing challenges in sensorless force feedback for minimally invasive procedures.

Contribution

The work introduces a novel deep learning model that fuses visual and tool data for force estimation, improving accuracy over single-modality approaches in surgical scenarios.

Findings

Combining video and tool data improves force estimation accuracy.

Force modeling for pulling tasks is more challenging than pushing.

A specialized loss function enhances force signal detail modeling.

Abstract

Providing force feedback as relevant information in current Robot-Assisted Minimally Invasive Surgery systems constitutes a technological challenge due to the constraints imposed by the surgical environment. In this context, Sensorless Force Estimation techniques represent a potential solution, enabling to sense the interaction forces between the surgical instruments and soft-tissues. Specifically, if visual feedback is available for observing soft-tissues' deformation, this feedback can be used to estimate the forces applied to these tissues. To this end, a force estimation model, based on Convolutional Neural Networks and Long-Short Term Memory networks, is proposed in this work. This model is designed to process both, the spatiotemporal information present in video sequences and the temporal structure of tool data (the surgical tool-tip trajectory and its grasping status). A series of analyses are carried out to reveal the advantages of the proposal and the challenges that remain for real applications. This research work focuses on two surgical task scenarios, referred to as pushing and pulling tissue. For these two scenarios, different input data modalities and their effect on the force estimation quality are investigated. These input data modalities are tool data, video sequences and a combination of both. The results suggest that the force estimation quality is better when both, the tool data and video sequences, are processed by the neural network model. Moreover, this study reveals the need for a loss function, designed to promote the modeling of smooth and sharp details found in force signals. Finally, the results show that the modeling of forces due to pulling tasks is more challenging than for the simplest pushing actions.