This work presents a method to enhance radial head localization in ultrasound imaging through landmark-based guidance. We created a dataset of ultrasound images containing three anatomical classes: radius, radial head, and humerus, recorded by an examiner. Using this dataset, we trained an nnU-Net model to perform real-time landmark detection, generating predictions at a rate of 20 frames per second. In two studies, participants without medical training were asked to locate their own radial head using a handheld ultrasound probe, first without guidance and then with visual and textual cues based on the model’s realtime segmentation. With this landmark-based guidance, 11 out of 12 participants successfully located the radial head, compared to 6 out of 12 without guidance. These results highlight the potential of landmark guidance to improve accuracy and usability in ultrasound interpretation, making it more accessible to users without medical training.