Recent Advances in Robot-Assisted Surgery: Soft Tissue Contact Identification

Nagy, Tamás Dániel; Haidegger, Tamás

doi:10.1109/saci46893.2019.9111599

Cited by 8 publications

(7 citation statements)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Taking the angle α in Equation (7) as the independent variable, the schematic image of Δf-α function is shown in Figure 4. To optimize the performance of the sensor, two goals of sensor parameters optimization are: (1) As shown in Figure 4, the difference between Δf 0°and Δf 45° is close to 0 (D1 is close to 0), to ensure that the angle change has the smallest impact on the detection result. (2) When tissues with different stiffness are tested, the difference between Δfi and Δfj should be larger than D1 to ensure that they can be reliably distinguished, as shown in D2 in Figure 4.…”

Section: A New Detection Methods and Its Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

A Piezoelectric Tactile Sensor for Tissue Stiffness Detection with Arbitrary Contact Angle

Zhang

Wei

et al. 2020

Sensors

View full text Add to dashboard Cite

In this paper, a piezoelectric tactile sensor for detecting tissue stiffness in robot-assisted minimally invasive surgery (RMIS) is proposed. It can detect the stiffness not only when the probe is normal to the tissue surface, but also when there is a contact angle between the probe and normal direction. It solves the problem that existing sensors can only detect in the normal direction to ensure accuracy when the degree of freedom (DOF) of surgical instruments is limited. The proposed senor can distinguish samples with different stiffness and recognize lump from normal tissue effectively when the contact angle varies within [0°, 45°]. These are achieved by establishing a new detection model and sensor optimization. It deduces the influence of contact angle on stiffness detection by sensor parameters design and optimization. The detection performance of the sensor is confirmed by simulation and experiment. Five samples with different stiffness (including lump and normal samples with close stiffness) are used. Through blind recognition test in simulation, the recognition rate is 100% when the contact angle is randomly selected within 30°, 94.1% within 45°, which is 38.7% higher than the unoptimized sensor. Through blind classification test and automatic k-means clustering in experiment, the correct rate is 92% when the contact angle is randomly selected within 45°. We can get the proposed sensor can easily recognize samples with different stiffness with high accuracy which has broad application prospects in the medical field.

show abstract

Section: A New Detection Methods and Its Optimizationmentioning

confidence: 99%

“…Since the lumps are often harder than the surrounding normal tissues, doctors can usually directly distinguish the lump by the difference in stiffness felt. [ 1 , 2 ] In order to distinguish different stiffness in RMIS, a tactile sensor for tissue stiffness detection is essential.…”

Section: Introductionmentioning

confidence: 99%

A Piezoelectric Tactile Sensor for Tissue Stiffness Detection with Arbitrary Contact Angle

Zhang

Wei

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…The amount of force applied to the tissues is a significant characteristic of surgery; it is important not to cause damage by excessive force, but it is also crucial to provide enough tension e.g., during tightening a knot [ 65 , 66 ]. Additionally, Trejos et al [ 53 ] have shown in their experimental study that force-based metrics correlate better with experience levels than temporal metrics in manual MIS.…”

Section: Performance Metricsmentioning

confidence: 99%

Performance and Capability Assessment in Surgical Subtask Automation

Nagy

Haidegger

2022

Sensors

View full text Add to dashboard Cite

Robot-Assisted Minimally Invasive Surgery (RAMIS) has reshaped the standard clinical practice during the past two decades. Many believe that the next big step in the advancement of RAMIS will be partial autonomy, which may reduce the fatigue and the cognitive load on the surgeon by performing the monotonous, time-consuming subtasks of the surgical procedure autonomously. Although serious research efforts are paid to this area worldwide, standard evaluation methods, metrics, or benchmarking techniques are still not formed. This article aims to fill the void in the research domain of surgical subtask automation by proposing standard methodologies for performance evaluation. For that purpose, a novel characterization model is presented for surgical automation. The current metrics for performance evaluation and comparison are overviewed and analyzed, and a workflow model is presented that can help researchers to identify and apply their choice of metrics. Existing systems and setups that serve or could serve as benchmarks are also introduced and the need for standard benchmarks in the field is articulated. Finally, the matter of Human–Machine Interface (HMI) quality, robustness, and the related legal and ethical issues are presented.

show abstract

“…For the proposed concept to operate properly, the indenter must be completely submerged in the sample. To do this, the material properties of the sample under investigation are important [8]. For example, the proposed concept cannot be applied to dense materials such as bone.…”

Section: Accuracymentioning

confidence: 99%

Displacement Measurement of Soft Material Indentation using Light Intensity

Hassan¹,

Omar²

2022

JMechE

View full text Add to dashboard Cite

This paper studies proof of concept in which soft material displacement during indentation is measured by a change in light intensity as the indenter is pushed into a soft material. The Light Dependent Resistor (LDR) sensor, which is embedded within a transparent hemispheric indenter, is used to measure the intensity of light while the indenter is on the surface and within the soft material. The change in light intensity contributes to a gradient that can be used to calculate the displacement of the soft material. The calculated difference in light intensity is converted into a displacement with the mean conversion factor obtained from the calibration equation. Measurements were carried out on three silicone samples with varying elastic properties over a range of different light intensities. By referring to linear regression and Rsquared values, the proposed concept has high consistency in measuring the difference of light intensity for various materials at different initial light intensities with more than 95% of R-squared values. In addition, the mean of variance between measurements, reflecting repeatability, is within an outstanding 1.5%. In the meantime, the accuracy of the proposed concept tested concerning the actual displacement is less than 10% of the variation. With promising results, the proposed design can be applied as a standalone technology or as an extension to a hand-held soft material characterization device for further improvement.

show abstract

Recent Advances in Robot-Assisted Surgery: Soft Tissue Contact Identification

Cited by 8 publications

References 61 publications

A Piezoelectric Tactile Sensor for Tissue Stiffness Detection with Arbitrary Contact Angle

A Piezoelectric Tactile Sensor for Tissue Stiffness Detection with Arbitrary Contact Angle

Performance and Capability Assessment in Surgical Subtask Automation

Displacement Measurement of Soft Material Indentation using Light Intensity

Contact Info

Product

Resources

About