Szczegóły publikacji
Opis bibliograficzny
Vibro-acoustic sensing of tissue-instrument-interactions allows a differentiation of biological tissue in computerised palpation / Thomas Sühn, Nazila Esmaeili, Moritz Spiller, Maximilian Costa, Axel Boese, Jessica Bertrand, Ajay Pandey, Christoph Lohmann, Michael FRIEBE, Alfredo Illanes // Computers in Biology and Medicine ; ISSN 0010-4825. — 2023 — vol. 164 art. no. 107272, s. 1–13. — Bibliogr. s. 12–13, Abstr. — Publikacja dostępna online od: 2023-07-19. — M. Friebe - dod. afiliacja: Otto-von-Guericke University, Magdeburg, Germany ; FOM University of Applied Sciences, Essen, Germany
Autorzy (10)
- Sühn Thomas
- Esmaeili Nazila
- Spiller Moritz
- Costa Maximilian
- Boese Axel
- Bertrand Jessica
- Pandey Ajay
- Lohmann Christoph Hubertus
- AGHFriebe Michael
- Illanes Alfredo
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 149521 |
|---|---|
| Data dodania do BaDAP | 2023-11-09 |
| Tekst źródłowy | URL |
| DOI | 10.1016/j.compbiomed.2023.107272 |
| Rok publikacji | 2023 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Computers in Biology and Medicine |
Abstract
Background: The shift towards minimally invasive surgery is associated with a significant reduction of tactile information available to the surgeon, with compensation strategies ranging from vision-based techniques to the integration of sensing concepts into surgical instruments. Tactile information is vital for palpation tasks such as the differentiation of tissues or the characterisation of surfaces. This work investigates a new sensing approach to derive palpation-related information from vibration signals originating from instrument-tissue-interactions. Methods: We conducted a feasibility study to differentiate three non-animal and three animal tissue specimens based on palpation of the surface. A sensor configuration was mounted at the proximal end of a standard instrument opposite the tissue-interaction point. Vibro-acoustic signals of 1680 palpation events were acquired, and the time-varying spectrum was computed using Continuous-Wavelet-Transformation. For validation, nine spectral energy-related features were calculated for a subsequent classification using linear Support Vector Machine and k-Nearest-Neighbor. Results: Indicators derived from the vibration signal are highly stable in a set of palpations belonging to the same tissue specimen, regardless of the palpating subject. Differences in the surface texture of the tissue specimens reflect in those indicators and can serve as a basis for differentiation. The classification following a supervised learning approach shows an accuracy of >93.8% for the three-tissue classification tasks and decreases to 78.8% for a combination of all six tissues. Conclusions: Simple features derived from the vibro-acoustic signals facilitate the differentiation between biological tissues, showing the potential of the presented approach to provide information related to the interacting tissue. The results encourage further investigation of a yet little-exploited source of information in minimally invasive surgery.