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.