Machine learning trains (14) to tune settings. For training (14), user interactions with image parameters (i.e., settings) as a part of ongoing examination of patients are used to establish (13) the ground truth positive and negative examples of settings instead of relying on an expert review of collected samples. Patient information, location information, and/or user information can also be included in training data so that a network is trained (14) to provide (18) settings for different situations based on the included information. During application, a patient is imaged (16). Moreover, an initial or a subsequent image is input (17) with other information (e.g., patient, user, and/or location information) to a machine-trained network to output (18) settings to be used for improved imaging in the situation.
TUNED MEDICAL ULTRASOUND IMAGING