Novel clinical device tracking and tissue event characterization using proximally placed audio signal acquisition and processing

Illanes, Alfredo; Boese, Axel; Maldonado, Iván; Pashazadeh, Ali; Schaufler, Anna; Navab, Nassir; Friebe, Michael

doi:10.1038/s41598-018-30641-0

Cited by 34 publications

(27 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Illanes et al proposed a novel method to characterize medical interventional devices insertion events by attaching an acoustic sensor to the proximal part of the apparatus [8]. They showed that the method allows to identify transitions between different types of tissues during needle insertion.…”

Section: Acoustic Emission Analysis For Medical Applicationsmentioning

confidence: 99%

Acoustic signal analysis of instrument–tissue interaction for minimally invasive interventions

Ostler¹,

Seibold

Fuchtmann³

et al. 2020

Int J CARS

Self Cite

View full text Add to dashboard Cite

Purpose Minimally invasive surgery (MIS) has become the standard for many surgical procedures as it minimizes trauma, reduces infection rates and shortens hospitalization. However, the manipulation of objects in the surgical workspace can be difficult due to the unintuitive handling of instruments and limited range of motion. Apart from the advantages of robot-assisted systems such as augmented view or improved dexterity, both robotic and MIS techniques introduce drawbacks such as limited haptic perception and their major reliance on visual perception. Methods In order to address the above-mentioned limitations, a perception study was conducted to investigate whether the transmission of intra-abdominal acoustic signals can potentially improve the perception during MIS. To investigate whether these acoustic signals can be used as a basis for further automated analysis, a large audio data set capturing the application of electrosurgery on different types of porcine tissue was acquired. A sliding window technique was applied to compute log-mel-spectrograms, which were fed to a pre-trained convolutional neural network for feature extraction. A fully connected layer was trained on the intermediate feature representation to classify instrument–tissue interaction. Results The perception study revealed that acoustic feedback has potential to improve the perception during MIS and to serve as a basis for further automated analysis. The proposed classification pipeline yielded excellent performance for four types of instrument–tissue interaction (muscle, fascia, liver and fatty tissue) and achieved top-1 accuracies of up to 89.9%. Moreover, our model is able to distinguish electrosurgical operation modes with an overall classification accuracy of 86.40%. Conclusion Our proof-of-principle indicates great application potential for guidance systems in MIS, such as controlled tissue resection. Supported by a pilot perception study with surgeons, we believe that utilizing audio signals as an additional information channel has great potential to improve the surgical performance and to partly compensate the loss of haptic feedback.

show abstract

Section: Acoustic Emission Analysis For Medical Applicationsmentioning

confidence: 99%

Acoustic signal analysis of instrument–tissue interaction for minimally invasive interventions

Ostler¹,

Seibold

Fuchtmann³

et al. 2020

Int J CARS

Self Cite

View full text Add to dashboard Cite

show abstract

“…A time-variant autoregressive modelling is used to track the frequency changes in the spectrum. For that, a pole-based approach is used where at each time instant the maximal energy pole is used for computation of the dominant frequency, as described in [8].…”

Section: Frequency Of Average Gray-level Changesmentioning

confidence: 99%

Frequency and average gray-level information for thermal ablation status in ultrasound B-Mode sequences

Ziegle

Illanes

Boese

et al. 2020

Current Directions in Biomedical Engineering

Self Cite

View full text Add to dashboard Cite

During thermal ablation in a target tissue the information about temperature is crucial for decision making of successful therapy. An observable temporal and spatial temperature propagation would give a visual feedback of irreversible cell damage of the target tissue. Potential temperature features in ultrasound (US) B-Mode image sequences during radiofrequency (RF) ablation in ex-vivo porcine liver were found and analysed. These features could help to detect the transition between reversible and irreversible damage of the ablated target tissue. Experimental RF ablations of ex-vivo porcine liver were imaged with US B-Mode imaging and image sequences were recorded. Temperature was simultaneously measured within the liver tissue around a bipolar RF needle electrode. In the B-Mode images, regions of interest (ROIs) around the centre of the measurement spots were analysed in post-processing using average gray-level (AVGL) compared against temperature. The pole of maximum energy level in the time-frequency domain of the AVGL changes was investigated in relation to the measured temperatures. Frequency shifts of the pole were observed which could be related to transitions between the states of tissue damage.

show abstract

“…A novel method for guiding medical interventional devices using an audio sensor attached to the tool's proximal end has been presented in [7,8]. Audio has shown promising results for acquiring non-invasively haptic information from medical tools.…”

Section: Introductionmentioning

confidence: 99%

Surgical audio information as base for haptic feedback in robotic-assisted procedures

Illanes

Schaufler

Sühn

et al. 2020

Current Directions in Biomedical Engineering

Self Cite

View full text Add to dashboard Cite

This work aims to demonstrate the feasibility that haptic information can be acquired from a da Vinci robotic tool using audio sensing according to sensor placement requirements in a real clinical scenario. For that, two potential audio sensor locations were studied using an experimental setup for performing, in a repeatable way, interactions of a da Vinci forceps with three different tissues. The obtained audio signals were assessed in terms of their resulting signal-to-noise-ratio (SNR) and their capability to distinguish between different tissues. A spectral energy distribution analysis using Discrete Wavelet Transformation was performed to extract signal signatures from the tested tissues. Results show that a high SNR was obtained in most of the audio recordings acquired from both studied positions. Additionally, evident spectral energy-related patterns could be extracted from the audio signals allowing us to distinguish between different palpated tissues.

show abstract

Novel clinical device tracking and tissue event characterization using proximally placed audio signal acquisition and processing

Cited by 34 publications

References 27 publications

Acoustic signal analysis of instrument–tissue interaction for minimally invasive interventions

Acoustic signal analysis of instrument–tissue interaction for minimally invasive interventions

Frequency and average gray-level information for thermal ablation status in ultrasound B-Mode sequences

Surgical audio information as base for haptic feedback in robotic-assisted procedures

Contact Info

Product

Resources

About