下腹部を対象とした極細針によるCTガイド下高正確度穿刺プランニング

早大学位記番号:新8149早稲田大

Toward Fully Automated Robotic Platform for Remote Auscultation

Since most developed countries are facing an increase in the number of patients per healthcare worker due to a declining birth rate and an aging population, relatively simple and safe diagnosis tasks may need to be performed using robotics and automation technologies, without specialists and hospitals. This study presents an automated robotic platform for remote auscultation, which is a highly cost-effective screening tool for detecting abnormal clinical signs. The developed robotic platform is composed of a 6-degree-of-freedom cooperative robotic arm, light detection and ranging (LiDAR) camera, and a spring-based mechanism holding an electric stethoscope. The platform enables autonomous stethoscope positioning based on external body information acquired using the LiDAR camera-based multi-way registration; the platform also ensures safe and flexible contact, maintaining the contact force within a certain range through the passive mechanism. Our preliminary results confirm that the robotic platform enables estimation of the landing positions required for cardiac examinations based on the depth and landmark information of the body surface. It also handles the stethoscope while maintaining the contact force without relying on the push-in displacement by the robotic arm.Comment: 8 pages, 11 figure

Suppression of Clothing-Induced Acoustic Attenuation in Robotic Auscultation

For patients who are often embarrassed and uncomfortable when exposing their breasts and having them touched by physicians of different genders during auscultation, we are developing a robotic system that performs auscultation over clothing. As the technical issue, the sound obtained through the clothing is often attenuated. This study aims to investigate clothing-induced acoustic attenuation and develop a suppression method for it. Because the attenuation is due to the loss of energy as sound propagates through a medium with viscosity, we hypothesized that the attenuation is improved by compressing clothing and shortening the sound propagation distance. Then, the amplitude spectrum of the heart sound was obtained over clothes of different thicknesses and materials in a phantom study and human trial at varying contact forces with a developed passive-actuated end-effector. Our results demonstrate the feasibility of the attenuation suppression method by applying an optimum contact force, which varied according to the clothing condition. In the phantom experiments, the attenuation rate was improved maximumly by 48% when applying the optimal contact force (1 N). In human trials, the attenuation rate was under the acceptable attenuation (40%) when applying the optimal contact force in all combinations in each subject. The proposed method promises the potential of robotic auscultation toward eliminating gender bias

Autonomous scanning motion generation adapted to individual differences in abdominal shape for robotic fetal ultrasound

The shortage of obstetricians and gynecologists is increasing in developed countries; therefore, there is a need to improve prenatal care procedures. To automate fetal ultrasound (US) imaging, this paper presents a deep neural network (DNN) model that generates US scan motions for a robot. Additionally, the robot can adapt to the abdominal surface even if the abdominal shape is not precisely known. The latent space of the DNN model is designed to represent the abdominal-shape information. The DNN model can predict the proper trajectory by mapping the height and width of the abdomen to the latent space. Moreover, the robot can detect any deviations from the correct trajectory and return to the right position. For validation, we performed a US scan using the proposed model on tissue-mimicked phantoms that were not used for training. Subsequently, we evaluated the number of dots that were wiped off by the robot from the phantom surface. Overall, the number of dots removed accounted for 91.7% of the total dots. The results demonstrated the feasibility of using the DNN model for motion generation. Hence, the proposed system has the potential to automate fetal US scans according to the individual differences in the abdominal shape.</p