Name
Abstract | ||
|---|---|---|
Haptic feedback relays important tissue mechanical properties to surgeons during open surgery. However, this information is lost during Robot-assisted Minimally Invasive Surgery (RMIS). Here we present a proof-of-concept for a novel instrument-integrated sensor that uses fiber Bragg grating (FBG) arrays to identify tissues based on mechanical properties. Subjects were tasked with sorting tissue phantoms based on hardness. When using a conventional surgical robot, the average error for novices (N=5) and the expert user was 22.5% and 12.5% respectively. This reduced to 2.5% and 0% when sorting with direct palpation by hand. In contrast, the senorized instrument with automated analysis was able to perform the task without any error across all trials. Clinical Relevance - The proposed sensor has the potential of identifying different tissues based on mechanical properties and thus characterize tumors and other relevant structures. It is envisaged that this will improve decision making process during RMIS and also provide useful sensory information for autonomous surgery. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/EMBC48229.2022.9871202 | Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) |
DocType | Volume | ISSN |
Conference | 2022 | 2694-0604 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Songping Sun | 1 | 0 | 0.34 |
| Erik P Dutson | 2 | 4 | 2.42 |
| Rory Geoghegan | 3 | 0 | 0.34 |