A Minimally Invasive Robotic Palpation Device

Introduction:: Robot-assisted minimally invasive surgery (RMIS) has emerged as an innovative approach for surgeons to perform complicated surgical procedures with improved dexterity, enhanced visualization, and high precision and accuracy while minimizing patients' trauma [1, 2]. Advanced robotic surgical systems, such as the da Vinci^® system (Intuitive Surgical, Inc.), offer several advantages, including increased degrees of freedom, high-definition visualization of the surgical site, and enhanced scalability [3]. However, one of the widely recognized limitations of robotic surgery is the lack of haptic feedback and the inability for the surgeon to perform manual palpation. Since pathological tissues, such as tumors and fibrotic tissues, are stiffer than healthy tissues, surgeons widely use manual palpation to diagnose the pathological tissues during open surgery. However, during RMIS, surgeons have to rely on visual information because the use of robot arms for surgical operations limits their ability to receive tactile feedback. Further, existing methods, such as pre-operative imaging modalities, frequently fail to identify diseased tissues, such as small tumors (smaller than ~1 cm in diameter) [4]. To address this limitation, we present a robotic tissue palpation device that can accurately, rapidly, and minimally invasively quantify tissue stiffness during robot-assisted surgery. We tested the device by accurately locating the tumor mimics in cancer models. Collectively, the results confirm that our device has the great potential to provide accurate and objective tissue stiffness assessments, allowing the surgeons to accurately and rapidly profile and detect the pathologic tissues with altered stiffness during robot-assisted surgery.


Materials and Methods:: A gelatin block (concentration: 10%, length: 35 mm, width: 35 mm, thickness: 10 mm) was prepared by dissolving gelatin powder (G2500, Sigma-Aldrich) in 1phosphate-buffered saline (1PBS; Gibco) at 70C and physically crosslinking the solutions at 4C for 30 min. To create tissue phantom with spatially heterogenous stiffness, such as a tumor surrounded by normal tissues, a cylindrical Indocyanine Green (ICG)-labeled poly(dimethylsiloxane) (PDMS) (ratio of prepolymer to crosslinker: 10:1; diameter: 12 mm, thickness: 2.5 mm) was embedded at the center of a gelatin block (concentration: 10% w/v).

Freshly isolated swine lungs were purchased from a local slaughterhouse (Dealaman Enterprise, Warren, NJ). The stiffness measurements of all lungs were conducted on the day of harvesting. To minimize the variability between the lungs resulting from different inflation and inhalation histories, the swine lungs were ventilated with a manual resuscitator (Ambu bag) connected to a pressure sensor for 10 min. To produce an ex vivo lung cancer (tumor) model, an incision was made in the distal region of the swine lung using a blade, and an ICG-labelled PDMS (diameter: 2 cm) that mimics a tumor was inserted into the subpleural region, approximately 5 mm below the pleura surface. The incised area was then sutured (4-0 silk) to prevent air leak and lung collapse.


Results, Conclusions, and Discussions:: The robotic palpation device is designed to intra-operatively and non-destructively quantify the stiffness of soft tissues (Fig. 1A). To determine the stiffness of tissue, the probe is directed downward perpendicular to the tissue surface (Fig. 1B, i). A force is then applied against the tissue surface via the probe to compress the tissue. The predefined magnitude of tissue deformation is confirmed when the electrical signal is detected across the contact electrodes (Fig. 1B, ii). Then, the elastic modulus (E) of the tissue is calculated by correlating the magnitude of the compression force (F_c) and the deformation length of the tissue (L_c) (Fig. 1C).

We first investigated whether the palpation device can profile tissues with spatially heterogenous stiffness, such as a tumor formed in tissue. To this end, an ICG-labeled cylindrical-shaped PDMS, which recapitulated a stiff tumor, was embedded at the central region of a soft 10% gelatin block (Fig. 1D). The generated stiffness map showed that while E values at peripheral areas of the phantom block varied between 23 to 27 kPa, there was a substantial increase in the E values (range: 38 to 50 kPa) at the center of the tissue phantom where the PDMS-based tumor was located (Fig. 1E). Further, we evaluated a lung tumor model that mimicked the presence of small tumor (size: 2 cm) in the distal regions of the lungs to determine whether our palpation device can accurately discriminate the tumor from healthy tissue (Fig 1F). Notably, the generated stiffness map showed that the E values ranged from 9.3 to 28.3 kPa, where the stiffness of 28.3 kPa corresponded to the precise location of the tumors in the lung lobe, highlighting the ability of the device to locate the small tumor in the lung (Fig. 1G).

Overall, the study highlights the potential of a novel robotic palpation device as an intraoperative tool that can assess and localize pathologic tissues with altered mechanical stiffness. The quantitative data provided by the palpation device can be interpreted along with the results of other pre- and intra-operative techniques to give clearer insights of tumors location and margins to surgeons.


Acknowledgements (Optional): : NSF Career Award (No. 2143620), New Jersey Health Foundation (IFHA 12-22)


References (Optional): : 1. Mir et al., A Minimally Invasive Robotic Tissue Palpation Device, Submitted to IEEE Transactions on Biomedical Engineering (2023).

2. V. Vitiello et al., “Emerging Robotic Platforms for Minimally Invasive Surgery,” IEEE Reviews in Biomedical Engineering, vol. 6, pp. 111-126, 2013.

3. O. M. Omisore et al., “A Review on Flexible Robotic Systems for Minimally Invasive Surgery,” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 52, no. 1, pp. 631-644, 2022.

4. I. S. Alam et al., “Emerging Intraoperative Imaging Modalities to Improve Surgical Precision”, Molecular Imaging and Biology, vol. 20, no. 5, pp. 705-715, 2018/10/01, 2018.