Multi-Modal Sensing in Colonoscopy: A Data-Driven Approach

Abstract

Soft optical sensors hold potential for enhancing minimally invasive procedures like colonoscopy, yet their complex, multi-modal responses pose significant challenges. This work introduces a machine learning (ML) framework for real-time estimation of 3D shape and contact force in a soft robotic sleeve for colonoscopy. To overcome limitations of manual calibration and collect large datasets for ML, we developed an automated platform for collecting data across a range of orientations, curvatures, and contact forces. A cascaded ML architecture was implemented for sequential estimation of contact force and 3D shape, enabling an accuracy with errors of 4.7% for curvature, 2.37% for orientation, and 5.5% for force tracking. We also explored the potential of ML for contact localization by training a model to estimate contact intensity and location across 16 indenters distributed along the sleeve. The force intensity was estimated with an error ranging from 0.06 N to 0.31 N throughout the indenters. Despite the proximity of the contact points, the system achieved high localization performances, with 8 indenters reaching over 80% accuracy, demonstrating promising spatial resolution.