Shape Sensing Techniques for Continuum Robots in Minimally Invasive Surgery: A Survey
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Continuum robots provide inherent structural compliance with high dexterity to access the surgical target sites along tortuous anatomical paths under constrained environments and enable to perform complex and delicate operations through small incisions in minimally invasive surgery. These advantages enable their broad applications with minimal trauma and make challenging clinical procedures possible with miniaturized instrumentation and high curvilinear access capabilities. However, their inherent deformable designs make it difficult to realize 3-D intraoperative real-time shape sensing to accurately model their shape. Solutions to this limitation can lead themselves to further develop closely associated techniques of closed-loop control, path planning, human-robot interaction, and surgical manipulation safety concerns in minimally invasive surgery. Although extensive model-based research that relies on kinematics and mechanics has been performed, accurate shape sensing of continuum robots remains challenging, particularly in cases of unknown and dynamic payloads. This survey investigates the recent advances in alternative emerging techniques for 3-D shape sensing in this field and focuses on the following categories: fiber-optic-sensor-based, electromagnetic-tracking-based, and intraoperative imaging modality-based shape-reconstruction methods. The limitations of existing technologies and prospects of new technologies are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it