This thesis explores the enhancement of ultrasound diagnostics through the integration of a robotic system with machine learning techniques. A customized, 3D-printed probe holder was designed to fit various probes and facilitate robotic manipulation for ultrasound imaging. Spatial calibration of the probe using an optical tracker and the fCal phantom ensures accurate probe positioning based on ultrasound image inputs. Hand-tool calibration enables the correct synchronization of the robotic end-effector with the probe. For motion planning, a deep reinforcement learning model was trained on ultrasound images synthesized from CT scans to automate probe positioning, thus minimizing the need for human intervention. The developed approach is expected to improve diagnostic precision, reduce operator dependency, and expand the clinical utility of ultrasound technology
