Tele-Operated Oropharyngeal Swab (TOOS) Robot Enabled by TSS Soft Hand for Safe and Effective Sampling

Efficacy and Safety of a Medical Robot for Non-Face-to-Face Nasopharyngeal Swab Specimen Collection: Nonclinical and Clinical Trial Findings for COVID-19 Testing

ObjectivesTo meet the high demand for polymerase chain reaction (PCR) tests to diagnose COVID-19 and rapidly control the outbreak, an efficient and safe molecular diagnostic protocol is necessary. In this study, we evaluated the efficacy and safety of the medical robot developed for non-face-to-face nasopharyngeal swab specimen collection.MethodsIn a nonclinical study, an otorhinolaryngologist collected swab specimens manually and using a medical robot. In a single-institution, randomized, open-label, prospective, exploratory clinical trial, nasopharyngeal swab specimens were collected from the enrolled participants both manually and by using the medical robot.ResultsEvaluation of the efficacy and safety of nasopharyngeal swab collection using a medical robot was assessed. After the operation of the robot, subjective discomfort experienced by the participants and any side effects or abnormalities in the nose were also monitored. Preliminary nonclinical data revealed comparable results between robotic and manual methods in terms of RNA metrics and cytokeratin-8 expression. Minor initial damage to A549 cells by the robot improved with subsequent use. In the clinical setting, the robot-assisted technique yielded a 92.31% detection rate for human RNase P, while the manual method achieved 100%. Post-swabbing discomfort reported by participants was similar for both methods and resolved within 48 h.ConclusionsThe medical robot system could efficiently, safely, and accurately collect nasopharyngeal swab samples in a non-face-to-face manner. Its installation in respiratory clinics, airports, or ports could minimize the infection risk between individuals and healthcare workers, thereby contributing to an efficient distribution of medical resources.

Adaptive Online Learning and Robust 3-D Shape Servoing of Continuum and Soft Robots in Unstructured Environments

In this article, we present a novel and generic data-driven method to servo-control the 3-D shape of continuum and soft robots based on proprioceptive sensing feedback. Developments of 3-D shape perception and control technologies are crucial for continuum and soft robots to perform tasks autonomously in surgical interventions. However, owing to the nonlinear properties of continuum robots, one main difficulty lies in the modeling of them, especially for soft robots with variable stiffness. To address this problem, we propose a versatile learning-based adaptive shape controller by leveraging proprioception of 3-D configuration from fiber Bragg grating (FBG) sensors, which can online estimate the unknown model of continuum robot against unexpected disturbances and exhibit an adaptive behavior to the unmodeled system without priori data exploration. Based on a new composite adaptation algorithm, the asymptotic convergences of the closed-loop system with learning parameters have been proven by Lyapunov theory. To validate the proposed method, we present a comprehensive experimental study using two continuum and soft robots both integrated with multicore FBGs, including a robotic-assisted colonoscope and multisection extensible soft manipulators. The results demonstrate the feasibility, adaptability, and superiority of our controller in various unstructured environments, as well as phantom experiments.

Deep Neural Network-Based Visual Feedback System for Nasopharyngeal Swab Sampling

During the 2019 coronavirus disease pandemic, robotic-based systems for swab sampling were developed to reduce burdens on healthcare workers and their risk of infection. Teleoperated sampling systems are especially appreciated as they fundamentally prevent contact with suspected COVID-19 patients. However, the limited field of view of the installed cameras prevents the operator from recognizing the position and deformation of the swab inserted into the nasal cavity, which highly decreases the operating performance. To overcome this limitation, this study proposes a visual feedback system that monitors and reconstructs the shape of an NP swab using augmented reality (AR). The sampling device contained three load cells and measured the interaction force applied to the swab, while the shape information was captured using a motion-tracking program. These datasets were used to train a one-dimensional convolution neural network (1DCNN) model, which estimated the coordinates of three feature points of the swab in 2D X-Y plane. Based on these points, the virtual shape of the swab, reflecting the curvature of the actual one, was reconstructed and overlaid on the visual display. The accuracy of the 1DCNN model was evaluated on a 2D plane under ten different bending conditions. The results demonstrate that the x-values of the predicted points show errors of under 0.590 mm from P0, while those of P1 and P2 show a biased error of about -1.5 mm with constant standard deviations. For the y-values, the error of all feature points under positive bending is uniformly estimated with under 1 mm of difference, when the error under negative bending increases depending on the amount of deformation. Finally, experiments using a collaborative robot validate its ability to visualize the actual swab's position and deformation on the camera image of 2D and 3D phantoms.

Comparison of the safety, effectiveness, and usability of swab robot vs. manual nasopharyngeal specimen collection

Background

Healthcare workers face a risk of infection during aerosol-generating procedures, such as nasal swabbing. Robot-assisted nasopharyngeal sampling aims to minimize this risk and reduce stress for healthcare providers. However, its effectiveness and safety require validation.

Methods

We conducted a controlled trial with 80 subjects at two teaching hospitals and compared robot-collected vs manually-collected nasopharyngeal swabs. The primary outcomes included specimen quality and success rate of nasopharyngeal swab collection. We also recorded the pain index, duration of the collection, and psychological stress using a post-collection questionnaire.

Results

During the study period, from September 23 to October 27, 2020, 40 subjects were enrolled in both the robotic and manual groups. The cycle threshold (Ct) value for nasopharyngeal specimens was statistically higher in the robotic group compared to the manual group (30.9 vs 28.0, p < 0.01). Both groups had Ct values under 35, indicating good quality specimens. In the robotic group, 3 out of 40 subjects required a second attempt at specimen collection, resulting in a success rate of 92.5 %. Further, although the pain levels were lower in the robotic group, the difference was not statistically significant (2.8 vs 3.6, p = 0.07). The manual group had a shorter sampling time, which was 29 s (201 vs 29, p < 0.05). However, when factoring in the time needed to put on personal protective equipment, the average time for the manual group increased to 251 s (201 vs 251, p < 0.05). Participants' questionnaire results show comparable psychological stress in both groups. Medical staff expressed that using a robot would reduce their psychological stress.

Conclusions

We propose a safe and effective robotic technology for collecting nasopharyngeal specimens without face-to-face contact, which may reduce the stress of physicians and nurses. This technology can also be optimized for efficiency, making it useful in situations where droplet-transmitted infectious diseases are a concern.