Force sensing and its application in minimally invasive surgery and therapy: A survey
Bibliographic record
Abstract
Abstract The reduced access conditions of minimally invasive surgery and therapy (MIST) impair or completely eliminate the feel of tool—tissue interaction forces. Many researchers have been working actively on the development of force sensors and sensing techniques to address this problem. The goal of this survey article is to summarize the state of the art in force sensing techniques for medical interventions in order to identify existing limitations and future directions. A literature search was performed from January to July 2009 using a combination of keywords relevant to the area, including force, sensor, sensing, haptics, and minimally invasive surgery. The literature search resulted in 126 articles with valuable content. This article presents a summary of the force sensing technologies, design specifications for force sensors in clinical applications, force sensors and sensing instruments that have been developed for MIST, and the experiments performed to determine the need for force information. Open areas of research include force sensor design, development of alternative methods of sensing, assessment of the impact of force information on performance, determination of the benefits of haptic information, and evaluation of the human factors involved in the processing and use of force information.
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.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".