Proprioceptive Sinus Endoscopy: Calibrating Rigid Endoscope Bending with a Novel Strain-Sensing Sleeve

Endoscopic Sinus surgery (ESS) suffers from significant targeting inaccuracies due to elastic deformation of the rigid endoscope during vision-based surgical procedures. Current vision-based image-guided surgery systems assume rigid-body behavior, neglecting clinically significant deflections caused when surgeons use the endoscope shaft as a fulcrum against anatomical structures. To address this fundamental limitation, we present a novel real-time deformation compensation system featuring a compact, instrumented sleeve that mounts proximally on the endoscope shaft, external to the surgical field. The sleeve integrates six strain gauges distributed at two axial locations and employs a hybrid compliant mechanism design—combining flexure elements with lever-amplification mechanisms—to precisely capture multi-axis bending. We evaluated four mapping algorithms, including polynomial regression and machine learning approaches, to optimize displacement prediction. Our system reduced displacement error from ≈ 7mm to ≈ 1mm RMSE, achieving 85% improvement in target localization accuracy. This work introduces a practical, cost-effective solution that addresses a fundamental source of error in endoscopic navigation without disrupting established surgical workflows.

Authors

Institutions

Publication Details

Journal
Journal of Medical Robotics Research
Published
2026-05-14
DOI
https://doi.org/10.1142/s2424905x2650008x
Primary Topic
Soft Robotics and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Proprioceptive Sinus Endoscopy: Calibrating Rigid Endoscope Bending with a Novel Strain-Sensing Sleeve

zhifan yan, Iulian Iordachita, Masaru Ishii, Manish Sahu et al.
Journal of Medical Robotics Research
Soft Robotics and Applications
article

Proprioceptive Sinus Endoscopy: Calibrating Rigid Endoscope Bending with a Novel Strain-Sensing Sleeve

zhifan yan, Iulian Iordachita, Masaru Ishii, Manish Sahu, Russell Taylor
article en

Abstract

Endoscopic Sinus surgery (ESS) suffers from significant targeting inaccuracies due to elastic deformation of the rigid endoscope during vision-based surgical procedures. Current vision-based image-guided surgery systems assume rigid-body behavior, neglecting clinically significant deflections caused when surgeons use the endoscope shaft as a fulcrum against anatomical structures. To address this fundamental limitation, we present a novel real-time deformation compensation system featuring a compact, instrumented sleeve that mounts proximally on the endoscope shaft, external to the surgical field. The sleeve integrates six strain gauges distributed at two axial locations and employs a hybrid compliant mechanism design—combining flexure elements with lever-amplification mechanisms—to precisely capture multi-axis bending. We evaluated four mapping algorithms, including polynomial regression and machine learning approaches, to optimize displacement prediction. Our system reduced displacement error from ≈ 7mm to ≈ 1mm RMSE, achieving 85% improvement in target localization accuracy. This work introduces a practical, cost-effective solution that addresses a fundamental source of error in endoscopic navigation without disrupting established surgical workflows.

Journal of Medical Robotics Research
Twitter (United States) (US)
Openalex Percentile: Top 7%
Soft Robotics and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.