Background/Aims: The factors affecting the detection rate of lymphoplasmacytic sclerosing pancreatitis (LPSP) using endoscopic ultrasound-guided tissue acquisition (EUS-TA) in patients with type 1 autoimmune pancreatitis (AIP) have not been thoroughly studied. Therefore, we conducted a retrospective study to identify the predictive factors for histologically detecting level 1 or 2 LPSP using EUS-TA. Methods: Fifty patients with AIP were included in this study, and the primary outcome measures were the predictive factors for histologically detecting level 1 or 2 LPSP using EUS-TA. Results: Multivariate analysis identified the use of fine needle biopsy (FNB) needles as a significant predictive factor for LPSP detection (odds ratio, 15.1; 95% confidence interval, 1.62–141; ¬¬p=0.017). The rate of good-quality specimens (specimen adequacy score ≥4) was significantly higher for the FNB needle group than for the fine needle aspiration (FNA) needle group (97% vs. 56%; p<0.01), and the FNB needle group required significantly fewer needle passes than the FNA needle group (median, 2 vs. 3; p<0.01). Conclusions The use of FNB needles was the most important factor for the histological confirmation of LPSP using EUS-TA in patients with type 1 AIP.

Background/Aims: The factors affecting the detection rate of lymphoplasmacytic sclerosing pancreatitis (LPSP) using endoscopic ultrasound-guided tissue acquisition (EUS-TA) in patients with type 1 autoimmune pancreatitis (AIP) have not been thoroughly studied. Therefore, we conducted a retrospective study to identify the predictive factors for histologically detecting level 1 or 2 LPSP using EUS-TA. Methods: Fifty patients with AIP were included in this study, and the primary outcome measures were the predictive factors for histologically detecting level 1 or 2 LPSP using EUS-TA. Results: Multivariate analysis identified the use of fine needle biopsy (FNB) needles as a significant predictive factor for LPSP detection (odds ratio, 15.1; 95% confidence interval, 1.62–141; ¬¬p=0.017). The rate of good-quality specimens (specimen adequacy score ≥4) was significantly higher for the FNB needle group than for the fine needle aspiration (FNA) needle group (97% vs. 56%; p<0.01), and the FNB needle group required significantly fewer needle passes than the FNA needle group (median, 2 vs. 3; p<0.01). Conclusions The use of FNB needles was the most important factor for the histological confirmation of LPSP using EUS-TA in patients with type 1 AIP.

Background/Aims: The factors affecting the detection rate of lymphoplasmacytic sclerosing pancreatitis (LPSP) using endoscopic ultrasound-guided tissue acquisition (EUS-TA) in patients with type 1 autoimmune pancreatitis (AIP) have not been thoroughly studied. Therefore, we conducted a retrospective study to identify the predictive factors for histologically detecting level 1 or 2 LPSP using EUS-TA. Methods: Fifty patients with AIP were included in this study, and the primary outcome measures were the predictive factors for histologically detecting level 1 or 2 LPSP using EUS-TA. Results: Multivariate analysis identified the use of fine needle biopsy (FNB) needles as a significant predictive factor for LPSP detection (odds ratio, 15.1; 95% confidence interval, 1.62–141; ¬¬p=0.017). The rate of good-quality specimens (specimen adequacy score ≥4) was significantly higher for the FNB needle group than for the fine needle aspiration (FNA) needle group (97% vs. 56%; p<0.01), and the FNB needle group required significantly fewer needle passes than the FNA needle group (median, 2 vs. 3; p<0.01). Conclusions The use of FNB needles was the most important factor for the histological confirmation of LPSP using EUS-TA in patients with type 1 AIP.

Objective: Practical applications of nerve decompression using neurosurgical robots remain unexplored. Our ongoing research and development initiatives, utilizing industrial robots, aim to establish a secure and efficient neurosurgical robotic system. The principal objective of this study was to automate bone grinding, which is a pivotal component of neurosurgical procedures. Methods: To achieve this goal, we integrated an endoscope system into a manipulator and conducted precision bone machining using a neurosurgical drill, recording the grinding resistance values across 3 axes. Our study encompassed 2 core tasks: linear grinding, such as laminectomy, and cylindrical grinding, such as foraminotomy, with each task yielding unique measurement data. Results: In linear grinding, we observed a proportional increase in grinding resistance values in the machining direction with acceleration. This observation suggests that 3-axis resistance measurements are a valuable tool for gauging and predicting deep cortical penetration. However, problems occurred in cylindrical grinding, and a significant error of 10% was detected. The analysis revealed that multiple factors, including the tool tip efficiency, machining speed, teaching methods, and deflection in the robot arm and jig joints, contributed to this error. Conclusion We successfully measured the resistance exerted on the tool tip during bone machining with a robotic arm across 3 axes. The resistance ranged from 3 to 8 Nm, with the measurement conducted at a processing speed approximately twice that of manual surgery performed by a surgeon. During the simulation of foraminotomy under endoscopic grinding conditions, we encountered a -10% error margin.

Objective: Practical applications of nerve decompression using neurosurgical robots remain unexplored. Our ongoing research and development initiatives, utilizing industrial robots, aim to establish a secure and efficient neurosurgical robotic system. The principal objective of this study was to automate bone grinding, which is a pivotal component of neurosurgical procedures. Methods: To achieve this goal, we integrated an endoscope system into a manipulator and conducted precision bone machining using a neurosurgical drill, recording the grinding resistance values across 3 axes. Our study encompassed 2 core tasks: linear grinding, such as laminectomy, and cylindrical grinding, such as foraminotomy, with each task yielding unique measurement data. Results: In linear grinding, we observed a proportional increase in grinding resistance values in the machining direction with acceleration. This observation suggests that 3-axis resistance measurements are a valuable tool for gauging and predicting deep cortical penetration. However, problems occurred in cylindrical grinding, and a significant error of 10% was detected. The analysis revealed that multiple factors, including the tool tip efficiency, machining speed, teaching methods, and deflection in the robot arm and jig joints, contributed to this error. Conclusion We successfully measured the resistance exerted on the tool tip during bone machining with a robotic arm across 3 axes. The resistance ranged from 3 to 8 Nm, with the measurement conducted at a processing speed approximately twice that of manual surgery performed by a surgeon. During the simulation of foraminotomy under endoscopic grinding conditions, we encountered a -10% error margin.

Objective: Practical applications of nerve decompression using neurosurgical robots remain unexplored. Our ongoing research and development initiatives, utilizing industrial robots, aim to establish a secure and efficient neurosurgical robotic system. The principal objective of this study was to automate bone grinding, which is a pivotal component of neurosurgical procedures. Methods: To achieve this goal, we integrated an endoscope system into a manipulator and conducted precision bone machining using a neurosurgical drill, recording the grinding resistance values across 3 axes. Our study encompassed 2 core tasks: linear grinding, such as laminectomy, and cylindrical grinding, such as foraminotomy, with each task yielding unique measurement data. Results: In linear grinding, we observed a proportional increase in grinding resistance values in the machining direction with acceleration. This observation suggests that 3-axis resistance measurements are a valuable tool for gauging and predicting deep cortical penetration. However, problems occurred in cylindrical grinding, and a significant error of 10% was detected. The analysis revealed that multiple factors, including the tool tip efficiency, machining speed, teaching methods, and deflection in the robot arm and jig joints, contributed to this error. Conclusion We successfully measured the resistance exerted on the tool tip during bone machining with a robotic arm across 3 axes. The resistance ranged from 3 to 8 Nm, with the measurement conducted at a processing speed approximately twice that of manual surgery performed by a surgeon. During the simulation of foraminotomy under endoscopic grinding conditions, we encountered a -10% error margin.