The present invention is directed to a method for health monitoring using one or more sensors comprising first measuring (100) a body composition via one or more sensors. The measured body composition is then classified (102) into one of a plurality of categories. An at least one setting to be used for the health monitoring is adjusted (104) based on the classified body composition. Then, the health monitoring is performed (106) using the adjusted at least one health monitoring setting, wherein at least one of the sensors used to measure the body composition may also be used to perform the health monitoring.

During acquisition of an ultrasound image feed, ultrasound control data frames are acquired that may be interspersed amongst the ultrasound data frames. The control data frames may use consistent reference scan parameters, irrespective of the scanner settings, and may not need to be converted to image frames. The control data frames can be passed to an artificial intelligence model, which predicts the suitable settings for scanning the anatomy that is being scanned. The artificial intelligence model can be trained with a dataset containing different classes of ultrasound control data frames for different settings, where substantially all the ultrasound control data frames in the dataset are consistently acquired using the reference scan parameters.

A system is described for generating diagnostic information from a video sequence of ultrasound images acquired in “blind sweeps”, i.e., without operator seeing ultrasound images as they are acquired. We disclose two different types of machine learning systems for predicting diagnostic information: a “Temporal Accumulation” system and a “3-D Modeling Component” system. These machine learning systems could be implemented in several possible ways: using just one or the other of them in any given implementation, or using both of them in combination. We also disclose a computing system which implements (a) an image selection system including at least one machine learning model trained to identify clinically suitable images from the sequence of ultrasound images and (b) an image diagnosis/measurement system including of one or more machine learning models, configured to obtain the clinically suitable images identified by the image selection system and further process such images to predict health states.

Provided are a medical diagnosis apparatus and a medical diagnosis method using the same. According to an embodiment, the medical diagnosis apparatus may include: a main body; a chair unit movably supported by the main body and on which an object is positioned; a diagnosis part that is movably connected to the main body and is spaced apart from the chair unit by a preset first distance in one plane; a controller configured to generate a control signal for moving the diagnosis part according to preset information; and a first driving device configured to generate a driving force for moving the diagnosis part according to the control signal.

An ultrasound imaging system includes a processor programmed to identify the type of tissue being imaged and to confirm that one or more system settings and/or the energy of ultrasound imaging signals delivered is set appropriately for such tissue. In one embodiment, an image obtained with the ultrasound imaging system is analyzed to determine if the tissue is ophthalmic (eye) tissue. If so, the system parameter settings and/or the transmit power of the signals produced by the ultrasound system are adjusted or maintained at a level that is appropriate for imaging such tissue.

The invention provides a method for determining a global confidence index for a 2D ultrasound image extracted from a 3D ultrasound volume, wherein the global confidence index indicates the suitability of the 2D ultrasound image for medical measure-ments. The method comprises obtaining a 3D ultrasound volume of a subject and extracting a set of at least one 2D ultrasound image from the 3D ultrasound volume. A set of geometrical indicators are then obtained with a first neural network, wherein each geometrical indicator indicates geometrical features of the anatomy of the subject. The set of 2D ultrasound images are then processed with a second neural network, wherein the output of the second neural network is a set of anatomical indicators and wherein the anatomical indicators indicate at least the presence of anatomical landmarks. A global confidence index is then determined for each one of the set of 2D ultrasound images based on the geometrical indicators and the anatomical indicators.

An ultrasound image processing apparatus (10) is disclosed comprising a processor arrangement (16) adapted to map a model (1) of an anatomical feature of interest onto an ultrasound image showing at least a section of said anatomical feature of interest and to segment said ultrasound image in accordance with the mapped model; and a touchscreen display (18, 19) adapted to display said ultrasound image including the mapped anatomical model. The processor arrangement is responsive to the touchscreen display and adapted to recognize a type of a user touch motion (3) provided through the touchscreen display (18, 19), each type of user touch motion being associated with a particular type of alteration of said mapping and alter said mapping in accordance with the recognized type of user touch motion. Also disclosed are an ultrasound imaging system, a computer-implemented method and a computer program product.

A system for recording ultrasound images comprises a memory comprising instruction data representing a set of instructions and a processor configured to communicate with the memory and to execute the set of instructions. The set of instructions, when executed by the processor, cause the processor to receive a data stream of two dimensional images taken using an ultrasound transducer and determine from the data stream that a feature of interest is in view of the transducer. The set of instructions further cause the processor to trigger an alert to be sent to a user to indicate that the feature of interest is in view of the transducer, and send an instruction to the transducer to trigger the transducer to capture a three dimensional ultrasound image after a predetermined time interval.

Photoacoustic and/or acousto-optical techniques may be used to transabdominally perform fetal oximetry and/or trans-abdominal fetal pulse oximetry. In some cases, a composite acoustic signal that has emanated from an abdomen of a pregnant mammal may be received by a processor from, for example, an ultrasonic detector and/or microphone positioned on, or near, a pregnant mammal's abdomen and the composite acoustic signal may result from an optical signal incident on the pregnant mammal's abdomen and a fetus contained therein. A portion of the composite acoustic signal that was incident on the fetus may be isolated from the composite acoustic signal and then analyzed to determine a fetal hemoglobin oxygen saturation level and/or a fetal tissue oxygen saturation level.

During acquisition of an ultrasound image feed, ultrasound control data frames are acquired that may be interspersed amongst the ultrasound data frames. The control data frames may use consistent reference scan parameters, irrespective of the scanner settings, and may not need to be converted to image frames. The control data frames can be passed to an artificial intelligence model, which predicts the suitable settings for scanning the anatomy that is being scanned. The artificial intelligence model can be trained with a dataset containing different classes of ultrasound control data frames for different settings, where substantially all the ultrasound control data frames in the dataset are consistently acquired using the reference scan parameters.