The demand for surgical robots has increased in recent years, accompanying the growing attention to minimally invasive surgery. To support the development of compact and cost-effective forceps for surgical robots, this paper proposes a novel forceps mechanism inspired by the pincers of crabs. The mechanism aims to reduce the number of components compared to conventional forceps, enabling a simpler, smaller, lighter, and compliant structure. Among organisms on Earth, crustaceans exhibit a high force-to-body-weight ratio, which led to our inspiration from crab chelae. The proposed mechanism, termed “Clawceps”, applies the crab’s scissor-like mechanism and incorporates the principle of leverage to stabilize force transmission. While the structure is compliant when no force is applied, the mechanism changes mode when the forceps are closed, engaging the joint to act as a fulcrum, thereby increasing the gripping force. The novel mechanism was evaluated using finite element analysis (FEA) with a PLA 3D-printed physical prototype. Models with and without a fulcrum were prepared to observe the gripping force. According to the FEA results, when the displacement was 1.5 mm, the gripping force of the model with a fulcrum was 7.8 N, while that without a fulcrum was 0.2 N. Experimental measurements with the actual prototype also showed significantly higher gripping force of the model with a fulcrum compared with that without a fulcrum, demonstrating the effectiveness of the mechanism. By implementing a mechanism that switches operation modes and transmits force using the principle of leverage, the Clawceps achieve both compliance and strong gripping force. This study successfully proved the concept of the proposed method to overcome a current limitation of compliant mechanism by incorporating principles derived from exoskeletal structures.

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Colonoscopy is the most effective procedure for cancer screening. However, insertion of a colonoscope into the large intestine remains challenging because of the complex and highly variable anatomy of the colon, particularly variations in its inner diameter. Various self-propelled colonoscopy robots have been proposed to overcome these difficulties. Nevertheless, several existing systems require complex mechanisms for adaptation to changes in the inner diameter of the colon, which increases structural complexity and limits practicality. Therefore, simpler and more adaptive propulsion mechanisms are required. In this paper, we propose a self-propelled colonoscopy robot with an elastic wall-fitting mechanism. This mechanism utilizes the elastic force of shape memory alloy (SMA) wires to adaptively conform to variations in the inner diameter of the colon, ensuring continuous contact with the intestinal wall. A prototype robot was fabricated using a 3D printer. Evaluation experiments were conducted using simulated intestinal tract models with inner diameters ranging from 30 to 60 mm to assess propulsion performance and thrust generation. The experimental results demonstrated that the proposed robot successfully self-propelled in all the simulated intestinal models. The robot achieved a maximum propulsive speed of 11 mm/s and generated sufficient thrust across the diameter range tested, indicating stable propulsion performance despite changes in the inner diameter. These results confirm that the proposed elastic wall-fitting mechanism effectively adapts to variations in the inner diameter of the colon without requiring complex mechanical structures. By leveraging the elastic properties of the SMA wires, the robot maintained consistent wall contact and stable propulsion, suggesting its potential applicability in safe and efficient colonoscopy procedures.

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This special issue represents a significant collaboration between the Japanese Society for Medical and Biological Engineering (JSMBE) and the Japan Society of Computer Aided Surgery (JSCAS), established under an official agreement between the two societies. The special issue has two primary objectives: (1) to publish cutting-edge original research by JSCAS members and introduce their outstanding contributions in medical applications to an international audience; and (2) to foster the exploration of new research directions among JSMBE members by highlighting the processes, implementation pathways, and future challenges associated with translating engineering technologies into practical clinical applications, particularly in the field of surgical assistance. The issue comprises nine original research articles and one invited review paper, all of which provide novel perspectives and valuable insights into the application of computers, robots, and advanced medical devices in surgery. Collectively, these contributions address several frontier topics in computer-aided surgery and can be broadly categorized into three areas: (1) surgical navigation and simulation, (2) robotic systems and surgical devices, and (3) advanced medical sensing and analysis. The invited review article also provides a comprehensive overview of recent developments in 3D Slicer, a widely used open-source platform for advanced medical image analysis.

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In the treatment of esophageal cancer, the high postoperative complication rate is an issue because of the extensive lymph node resection for curative purposes. To optimize the extent of resection, we developed a forceps-type coincidence radiation detector based on the concept of positron emission tomography (PET) for precise intraoperative localization of metastases. The forceps-type coincidence radiation detector consists of a pair of miniaturized PET detectors mounted on the tip of the forceps. The forceps are clamped over a suspected metastatic site intraoperatively to measure the radiation and identify the site of metastasis based on 18F-labeled fluorodeoxyglucose accumulation above a certain threshold. However, the sensitivity of the forceps-type coincidence radiation detector depends on the distance between the pair of detectors mounted on the tips of the forceps, and maintaining a constant distance between these detectors is difficult because the handle is held in the palm of the surgeon operating the device. Achieving stable coincidence counting requires correction of the coincidence radiation sensitivity according to the distance between the detectors during measurement. Utilizing the fact that the distance between detectors depends on the tip angle of the forceps, we developed a system that executes the correction based on the forceps tip angle. A linear encoder was used to measure the displacement of the forceps shaft and estimate the forceps tip angle from the amount of displacement. We also evaluated the effect of correcting radiation detection sensitivity using an experimentally developed system. The radiation detection sensitivity was corrected to a mean (± standard deviation) dose of 22.3 ± 1.3 kBq compared to the true value of 21.3 kBq. The average correction error was 5%, indicating the correction capability of the developed system. However, the variations in correction values pose a risk of missing metastases. In the future, the accuracy of the correction system will be improved, and experiments will be conducted in the clinical environment.

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Modern hepatectomies are planned along anatomical segments of the liver, and intraoperative injection of indocyanine green enables visualization of these segments using near-infrared fluorescence (NIRF) laparoscopy. However, subsegmental resections that extend partially beyond boundaries of the segments are increasingly attempted in patients with liver insufficiency, requiring additional anatomical understanding of the lesions. Recently, augmented reality (AR) has gained attention as a technique for overlaying preoperative anatomical information directly onto the surgical field. Nonetheless, achieving real-time and precise alignment of preoperative data onto surgical objects remains a challenge. To address this issue, we propose an AR system using a multimodal tissue marker detectable by both X-ray and NIRF, and an object-detection machine-learning model (YOLOv9). The multimodal AR marker was administered to excised porcine liver samples, and their NIRF laparoscopic and X-ray computed tomography (CT) images were acquired. Marker coordinates in the NIRF laparoscopic images were obtained using the YOLOv9 model, and the correspondence between markers in the CT and NIRF views were automatically determined using our original algorithm. CT marker images were reprojected onto the laparoscopic images based on their coordinates. The system achieved an 80.2% marker detection success rate in NIRF images, a 78.4% registration success rate, and a mean reprojection error of 3.9 pixels, with an average processing speed of 28.9 fps. These results demonstrate the potential of our proposed system to realize real-time AR navigation during laparoscopic liver surgeries. Although clinical application would require a hybrid operating room equipped with CT at the time of marker administration, this technology has the potential to superimpose internal structures of the liver onto the organ surface in the laparoscopic view on video monitors.

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Minimally invasive laparoscopic surgery is widely used because it reduces patient burden compared with open surgery, and robotic assistance has further improved surgical precision and ergonomics. However, conventional laparoscopy is limited by a narrow field of view, creating blind spots that can lead to perioperative complications. To overcome this limitation, we developed a planar follower robot equipped with multiple cameras to provide multi-angle intra-abdominal visualization. The system integrates an field programmable gate array (FPGA)-based multi-camera transmission board capable of transmitting eight video streams at 30 fps, together with a leader-follower mechanism that enables intuitive shape control of the planar robot. Experimental evaluation showed that all eight camera feeds were acquired stably for more than 8 hours, with an inter-camera delay of approximately 10 ms and an end-to-end latency of 230 ms. In a laparoscopic training box, real-time viewpoint adjustment with the leader device allowed visualization of forceps tips and hidden regions that are not visible with a conventional laparoscope. These results demonstrate that the system can flexibly adjust camera perspectives and reduce blind spots, supporting both surgeon-operated and robot-controlled procedures. Although the current prototype remains limited by camera configuration and insertion size, this study establishes the feasibility and potential utility of a planar multi-camera robot for enhancing intraoperative visualization.

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A thermal endoscope capable of controlling tumor temperature has been developed to achieve minimally invasive endoscopic photothermal therapy (PTT) with high therapeutic efficacy. However, the tumor temperature cannot be controlled at short measurement distances from the heated target. We have proposed a temperature estimation method to control tumor temperature even at a close range of the target during endoscopic examination. The proposed method estimates the temperature of the laser irradiation point by measuring the temperatures of discrete points around the laser irradiation point. In this study, we verified the measurement principle of this method. We assumed that the temperature distribution on the surface of the target follows the Gaussian function. In the proposed method, the temperature at the laser irradiation point was estimated from the temperatures measured at three points around the laser irradiation point. A temperature estimation experiment was conducted by targeting a circular silicone rubber disk heated at the center with an electric heating wire using a thermal imaging sensor. The temperature of the heated point was set at 40-60℃, and the measurement distance was set at 10-50 mm. The coordinates of the three points used for estimation were initially set at (x₁, x₂, x₃) = (−6.4 mm, −3.2 mm, 4.9 mm) from the heated point. We also evaluated point configuration scaled down to 0.8 times and up to 1.2 times the original size. Linearity was observed between the heating point temperature and the estimated temperature. Temperature estimation at approximately 43℃, which was the target temperature of PTT, was possible when the measurement distance was 20 mm or less. These results suggest that the heating point temperature can be estimated at short distances from the target using the proposed method.

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This study investigated methods for accurately determining the appropriate timing for changing ostomy appliance using image processing and machine learning. In ostomy users, if urine or stool leaks during use and enters the space between the stoma and the ostomy appliance, the appliance may detach or leak. Therefore, changing ostomy appliance at the appropriate time is critically important. However, nurses and patients unfamiliar with changing ostomy appliance may misjudge the timing for replacement. In the first phase of this study, we attempted to identify the suitable timing for stoma appliance replacement using image processing and machine learning. Specifically, we implemented the following steps: 1. converted color images to grayscale images, 2. reduced the resolution of grayscale images, 3. applied a Gaussian filter, 4. locally optimized the kernel size and standard deviation, and 5. locally optimized the number of epochs. In the second phase, we demonstrated that augmentation of misclassified data improved prediction rates in both the timing requiring replacement and timing not requiring replacement. Furthermore, the false negative rate, a standard evaluation metric in machine learning, decreased by 10%, and the F1 score increased by 2.6 for the timing requiring replacement and by 1.4 for the timing not requiring replacement.

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Laparoscopic surgery is technically demanding and requires extensive training to master. Simulation-based training methods provide the advantage of repeated practice in a virtual environment. While previous studies have developed various simulation methods, modeling the interaction between multiple connected organs has not been sufficiently investigated. In this study, we propose a method that simulates the deformation of multiple organs, specifically the liver and gallbladder, using octree-based cube structures. We also model the connective tissue linking the two organs and simulate gallbladder dissection. Organ deformations are computed using octree cube structures with shape constraints, and surface vertices are updated by trilinear interpolation within a position based dynamics framework. The connective tissue between organs is represented by numerous textured transparent spheres. Instead of computing forces for each sphere, the cube elements are connected by springs. The strength of each connection is determined by the number of surrounding particles. As the electro hook approaches, the number of fat particles decreases and the connection is eventually released. This approach introduces a mechanism to simulate the interactions between multiple cube structures via connections, which was not used in previous methods. The proposed method successfully simulated the dissection of connective tissue between the liver and gallbladder, reproducing organ deformation and dynamic interaction in real time at mean frame rate of 31.1 fps. Experimental results demonstrated that our approach can reproduce the gallbladder removal process in laparoscopic cholecystectomy. In conclusion, we proposed a method to simulate the gallbladder removal process using octree-based cube structures and textured transparent particles. Simulation studies demonstrated the potential of this method for surgical simulation and training applications.

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