Sensor-free contact force guidance for ultrasound imaging

This study presents a novel sensor-free force estimation technique for automatic control of contact force in robotic ultrasound scanning. By employing a deep learning model that combines Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) architectures, the system estimates force differences using sequences of ultrasound images, bypassing the need for complex sensor installations. Experiments conducted without force sensors demonstrated the model's accuracy, achieving a mean squared error (MSE) of less than 0.6 N². This approach simplifies the robotic ultrasound setup and enhances imaging performance.