Provided is an ultrasound diagnosis apparatus including an image processor configured to generates an ultrasound image on the basis of an ultrasound signal, an image outputter configured to display the ultrasound image generated by the image processor on the basis of a plurality of parameters, a sound outputter configured to output Doppler sound of the ultrasound image, and a controller configured to control a volume of the Doppler sound on the basis of at least one of the plurality of parameters.

A method for uterine activity monitoring may include: acquiring a plurality of signals from a plurality of sensors during uterine activity; processing the plurality of signals to extract a plurality of uterine electrical activity characteristics; analyzing the plurality of uterine electrical activity characteristics; and classifying the uterine activity as one of: a preterm labor contraction, a labor contraction, a Braxton-Hicks contraction, and a state of no contraction. A method of assessing over time a pre-term birth risk of a pregnant female may include: calculating a baseline pre-term birth risk score based on a user input; acquiring, over time, a signal from a sensor; analyzing the signal to extract a parameter of interest, such that the parameter of interest comprises a physiological parameter; and calculating an instant pre-term birth risk score based, at least in part, on the parameter of interest and the user input.

The invention relates to a computer-implemented method for automatically detecting anatomical structures (3) in a medical image (1) of a subject, the method comprising applying an object detector function (4) to the medical image, wherein the object detector function performs the steps of: (A) applying a first neural network (40) to the medical image, wherein the first neural network is trained to detect a first plurality of classes of larger-sized anatomical structures (3a), thereby generating as output the coordinates of at least one first bounding box (51) and the confidence score of it containing a larger-sized anatomical structure; (B) cropping (42) the medical image to the first bounding box, thereby generating a cropped image (11) containing the image content within the first bounding box (51); and (C) applying a second neural network (44) to the cropped medical image, wherein the second neural network is trained to detect at least one second class of smaller-sized anatomical structures (3b), thereby generating as output the coordinates of at least one second bounding box (54) and the confidence score of it containing a smaller-sized anatomical structure.

An ultrasonic diagnostic imaging system is used to acquire a fetal image in a sagittal view for the performance of a nuchal translucency measurement. After a fetal image has been acquired, a zoom box is positioned over the image, encompassing a region of interest. The size of the zoom box is automatically set for the user in correspondence with gestational age or crown rump length. The system automatically tracks the region of interest within the zoom box in the presence of fetal motion in an effort to maintain the region of interest within the zoom box despite movement by the fetus.

A patient monitoring system includes: a biomedical sensor including: a transducer configured to produce a signal corresponding to a biological function; a sensor converter configured to convert the signal to a converted signal; and a transmitter configured to produce a communication, based on the converted signal, that is indicative of one or more values of the biological function, and to send the communication wirelessly; and a base station including: a receiver configured to receive the communication wirelessly and to produce a receiver output signal; a base station interface configured to produce a base station output signal indicative of the one or more values of the biological function; and at least one output port to receive the base station output signal and configured to be hard-wire connected to a display that is configured to display information indicative of the biological function.

Examples disclosed herein relate to determining a power difference in sensor signals. Examples include a first sensor to transmit a first ultrasonic signal into a pregnant woman and to receive a second ultrasonic signal; and a second sensor to transmit a third ultrasonic signal into the pregnant woman and to receive a fourth ultrasonic signal. A processing resource determines a first power difference of the first sensor according to a difference between respective powers of the first ultrasonic signal and the second ultrasonic signal and is to determine a second power difference of the second sensor according to a difference between respective power of the third ultrasonic signal and the fourth ultrasonic signal. In examples, the processing resource is to determine a relative location of the fetal heart according to a comparison of the first power difference and the second power difference.

Disclosed in the present invention are a method and an apparatus for identification of imaging quality of fetal ultrasound images, the method including: acquiring parameters of fetal ultrasound images, used for identification of imaging quality of fetal ultrasound images; identifying an imaging score of fetal ultrasound images based on the parameters thereof; and identifying the imaging quality thereof based on the imaging score thereof. Obviously, it may lead to a quick and accurate identification of the imaging quality of fetal ultrasound images by automatically identifying the imaging quality thereof based on the imaging score thereof, thereby realizing quick and accurate management of the imaging quality thereof so as to facilitate to acquire fetal ultrasound images with high quality, which facilitates the acquisition of accurate fetal growth and development and may have an acknowledgment of the operational standardization of the personnel during the detection of fetal ultrasound images.

Ultrasound image devices, systems, and methods are provided. An ultrasound imaging system comprising a processor circuit in communication with an ultrasound transducer array, the processor circuit configured to receive, from the ultrasound transducer array, a set of images of a three-dimensional (3D) volume of a patients anatomy including an anatomical feature; obtain first measurement data of the anatomical feature in a first image of the set of images; generate second measurement data for the anatomical feature in one or more images of the set of images by propagating the first measurement data from the first image to the one or more images; and output, to a display in communication with the processor circuit, the second measurement data for the anatomical feature.

Systems and methods are provided for enhanced heart medical imaging operations, particularly as by incorporating use of artificial intelligence (AI) based fetal heart functional analysis and/or real-time and automatic ejection fraction (EF) measurement and analysis.

Disclosed in the present invention are a method and apparatus for the identification of fetal cross-sections based on ultrasound dynamic images; the method includes: inputting sequentially each frame of fetal ultrasound images from acquired multiple consecutive frames of fetal ultrasound images into a predetermined feature-detecting model for analysis; acquiring a sequentially exported analysis from the feature-detecting model as feature information for each frame of fetal ultrasound images; corresponding to each frame of fetal ultrasound images, identifying a cross-section by the categories of the part and the structural feature. Obviously, for a fetal ultrasound image, the implementation of the present invention may improve the identified accuracy, identified efficiency, and the standardization of the cross-section, by acquiring the part features and structural features from consecutive multi-frame fetal ultrasound images and identifying the cross-section by combining the part features and structural features.