Abstract: A method for obtaining a medical image at a target plane. The method comprises obtaining a three-dimensional, 3D, volume containing an anatomical structure and identifying the anatomical structure in the 3D volume. The geometry of the anatomical structure in the 3D volume is analyzed and the target plane is registered to the 3D volume based on the analysis of the geometry of the anatomical structure. The medical image is extracted from the 3D volume at the registered target plane.

Abstract: A method for acquiring a set of target planar views through an anatomy of interest through processing of 3D ultrasound image data. The method processes received 3D image frames in real time to extract target planes and to process each extracted plane with a quality check algorithm. A record is kept of all target planes which have been acquired with sufficient quality, for example by removing them from a list of planes still to be captured. Optionally, each new frame need only be processed to extract planes which are yet to be acquired with sufficient quality. A user interface display is dynamically controlled so that a user is always presented with a real-time display of a planar view of at least one of the planes still to be captured with sufficient quality. The user is thus continually provided the optimal guidance imagery for controlling the acquisition apparatus to acquire new 3D frames which include image data from which the still required 2D planes can be successfully extracted with high quality.

Abstract: A medical imaging and segmentation method comprising utilizing segmentation results obtained for 3D frames earlier in a same imaging session to initialize a segmentation model for subsequent 3D frames. This accelerates and stabilizes segmentation results, particularly in the context of a scanning procedure in which segmentation is performed in real time (on-the-fly) for each acquired frame.

Abstract: The present invention relates to computer implemented methods and systems for processing medical image. The methods include receiving (101) a first acquired image of a patient, wherein the first acquired image is a medical image acquired using a medical imaging device. The first acquired image is compared (102) to a plurality of reference images by determining a first plurality of confidence metrics using a first calculation method. The first plurality of confidence metrics for the first acquired image are output (103). Each respective confidence metric of the first plurality of confidence metrics indicates how closely the first acquired image matches a respective reference image of the plurality of reference images.

Abstract: A method and system for providing saliency maps for a deep learning model. The method comprises inputting an input image into the deep learning model trained to output a metric score, from a plurality of metric scores, for the input image. A supportive saliency map is generated for the input image, from the deep learning model, corresponding to a first range of the metric scores for the image, thereby providing one or more supportive regions of the image indicative of the first range of metric scores. A distractive saliency map is also generated for the image, from the deep learning model, corresponding to a second range of the metric scores for the image, thereby providing one or more distractive regions of the image indicative of the second range of metric scores.

Abstract: A method (100) for classifying a patient's diastolic function, comprising: (i) receiving (120), from an ultrasound device (280), a plurality of 2D echocardiographic images of the patient's heart; (ii) analyzing (150), by a trained diastolic function prediction algorithm, the plurality of 2D echocardiographic images of the patient's heart to estimate left ventricular end-diastolic pressure (LVEDP); (iii) classifying (160) the patient's diastolic function as normal or abnormal based on the estimated LVEDP; and (iv) providing (170), to a user via a user interface, an indication of the patient's diastolic function as normal or abnormal.

Abstract: A method and system for automatically detecting within an input 3D ultrasound image one or more suspected disease features and automatically computing an optimum one or more planes through the 3D field of view for acquiring imagery that would best assist in confirming or further analyzing the suspected disease features. The determined optimum one or more planes are used to control acquisition by an ultrasound acquisition system of new 2D images which correspond to said determined optimum planes.

Abstract: A method (100) for providing an analysis of coronary artery disease (CAD), comprising: (i) receiving (120) patient metadata about the patient; (ii) receiving (130) a temporal sequence of 2D and/or 3D ultrasound images of the patient's heart; (iii) selecting (140), by the CAD prediction system, a plurality of ultrasound images from the temporal sequence; (iv) processing (150), using a trained AI algorithm of the CAD prediction system, the selected plurality of ultrasound images to generate a feature map of the selected plurality of ultrasound images; (v) analyzing (160) the generated feature map and the received patient metadata using a trained algorithm of the CAD prediction system to generate a CAD prediction output; (vi) providing (170), via a user interface of the CAD prediction system, the generated CAD prediction output.

Abstract: A system and method for providing an enhancing or contrast agent advisability indicator. Subject data for a subject is processed using an AI-based model to obtain an indication of whether a medical imaging procedure for the subject requires an enhancing or contrast agent. The enhancing or contrast agent advisability indicator is generated with respect to one or more target pathologies assessed in the medical imaging procedure.

Abstract: A computer implemented method for collating patient data for analysis comprises receiving a set of input data comprising a plurality of patient data records, wherein the plural patient data records comprise medical imaging data and at least one other patient data type; and generating a vector for each of the plural patient data records by processing each patient data record using a corresponding encoding algorithm, wherein the encoding algorithm used to generate the vector is selected based on the type of patient data record and wherein the vectors are for use by a machine learning model.