Search

Robotic surgery is preferred over open or laparoscopic surgeries due to its intuitiveness and convenience. However, prolonged use of surgical robots can cause neck pain and joint fatigue in wrist and fingers. Also, input systems are bulky and difficult to maintain. To resolve these issues, we propose a novel input module based on real-time 3D hand tracking driven by RGB images and MediaPipe framework to control surgical robots such as patient side manipulator (PSM) and endoscopic camera manipulator (ECM) of da Vinci research kit. In this paper, we explore the mathematical basis of the proposed 3D hand tracking module and provide a proof-of-concept through user experience (UX) studies conducted in a virtual environment. End-to-end latencies for controlling PSM and ECM were 170 ± 10 ms and 270 ± 10 ms, respectively. Of fifteen novice participants recruited for the UX study, thirteen managed to reach a qualifiable level of proficiency after 50 min of practice and fatigue of hand and wrist were imperceivable. Therefore, we concluded that we have successfully developed a robust 3D hand tracking module for surgical robot control and in the future, it would hopefully reduce hardware cost and volume as well as resolve ergonomic problems. Furthermore, RGB image driven 3D hand tracking module developed in our study can be widely applicable to diverse fields such as extended reality (XR) development and remote robot control. In addition, we provide a new standard for evaluating novel input modalities of XR environments from a UX perspective. [ABSTRACT FROM AUTHOR]

Robot-assisted surgery platforms are utilized globally thanks to their stereoscopic vision systems and enhanced functional assistance. However, the necessity of ergonomic improvement for their use by surgeons has been increased. In surgical robots, issues with chronic fatigue exist owing to the fixed posture of the conventional stereo viewer (SV) vision system. A head-mounted display was adopted to alleviate the inconvenience, and a virtual vision platform (VVP) is proposed in this study. The VVP can provide various critical data, including medical images, vital signs, and patient records, in three-dimensional virtual reality space so that users can access medical information simultaneously. An availability of the VVP was investigated based on various user evaluations by surgeons and novices, who executed the given tasks and answered questionnaires. The performances of the SV and VVP were not significantly different; however, the craniovertebral angle of the VVP was 16.35° higher on average than that of the SV. Survey results regarding the VVP were positive; participants indicated that the optimal number of displays was six, preferring the 2 × 3 array. Reflecting the tendencies, the VVP can be a neoconceptual candidate to be customized for medical use, which opens a new prospect in a next-generation surgical robot. [ABSTRACT FROM AUTHOR]

To determine the sources and pathways of lead (Pb) and zinc (Zn) in river sediments contaminated with metals from mining and smelting activities, metal concentrations and Pb and Zn isotope ratios were measured in river water and sediment, and potential metal contaminant samples (imported Zn concentrates, smelting wastes, soils around the smelter, mine ores, and riverside tailings). Zn and cadmium (Cd) concentrations in river water and sediment samples were 30- and 11–25-fold higher, respectively, near the smelter than upstream, while a 6-fold increase in sediment Pb concentrations was detected over the same region. Sediment samples near the smelter (207Pb/206Pb = 0.8638 and 208Pb/206Pb = 2.0960) were observed to have a different Pb isotopic composition from upstream of the smelter (207Pb/206Pb = 0.8322 and 208Pb/206Pb = 2.0502), with δ66Zn values increasing from –0.01 to 0.82‰. Analysis of Pb and Zn isotopes and concentrations revealed that dust-contaminated soils were a major Pb source, and baseline sediments were found to be contaminated by regional mining tailings. For Zn in sediments, the main Zn sources were groundwater-derived Zn (δ66Zn = 1.02 ± 0.43‰, n = 4), dust-contaminated soils (δ66Zn = –0.18 ± 0.08‰, n = 3), and tailings-contaminated sediments (δ66Zn = 0.01 ± 0.07‰, n = 10). Endmember mixing model results showed that dust-contaminated soils contributed 78% and 64% of sediment Pb and Zn, respectively, within 2 km of the Zn smelter, decreasing to negligible levels after 47.1 km downstream. Downstream of the smelter, groundwater-derived Zn contributed 54% of sediment Zn, whereas tailings contaminated sediments contributed 70% and 25% of Pb and Zn, respectively. [ABSTRACT FROM AUTHOR]

The intraoperative estimated blood loss (EBL), an essential parameter for perioperative management, has been evaluated by manually weighing blood in gauze and suction bottles, a process both time-consuming and labor-intensive. As the novel EBL prediction platform, we developed an automated deep learning EBL prediction model, utilizing the patch-wise crumpled state (P-W CS) of gauze images with texture analysis. The proposed algorithm was developed using animal data obtained from a porcine experiment and validated on human intraoperative data prospectively collected from 102 laparoscopic gastric cancer surgeries. The EBL prediction model involves gauze area detection and subsequent EBL regression based on the detected areas, with each stage optimized through comparative model performance evaluations. The selected gauze detection model demonstrated a sensitivity of 96.5% and a specificity of 98.0%. Based on this detection model, the performance of EBL regression stage models was compared. Comparative evaluations revealed that our P-W CS-based model outperforms others, including one reliant on convolutional neural networks and another analyzing the gauze’s overall crumpled state. The P-W CS-based model achieved a mean absolute error (MAE) of 0.25 g and a mean absolute percentage error (MAPE) of 7.26% in EBL regression. Additionally, per-patient assessment yielded an MAE of 0.58 g, indicating errors < 1 g/patient. In conclusion, our algorithm provides an objective standard and streamlined approach for EBL estimation during surgery without the need for perioperative approximation and additional tasks by humans. The robust performance of the model across varied surgical conditions emphasizes its clinical potential for real-world application. [ABSTRACT FROM AUTHOR]