Abstract: According to an aspect, there is provided a computer implemented method in a central sewer of selecting a training dataset with which to train a model using a distributed machine learning process, wherein the training dataset is to comprise medical data that satisfies one or more clinical requirements and wherein training data in the training dataset is located at a plurality of clinical sites. The method comprises requesting (302) from each of the clinical sites, metadata describing features of matching data at the respective clinical site that satisfies the one or more clinical requirements. The method then comprises determining (304), from the metadata, a measure of variation of the features of the matching data. Based on the measure of variation, the method then comprises selecting (306) training data for the training dataset from the matching data using the metadata.

Abstract: An ultrasound data processor for assessing reliability of ultrasound data for determining blood flow values for a patient. The processor is adapted to apply at least a spectral analysis procedure and a wavelet decomposition procedure to input ultrasound data (or data or signals derived therefrom), and to feed the output(s) of said procedures to a classifier algorithm configured to generate a reliability indicator based on these input information. Both spectral analysis and wavelet decomposition are signal analysis techniques which assess periodic properties of data and signals, this being particularly suited to detecting background noise artefacts (such as those generated by electrical knives used in surgery) which do not change the morphology or shape of the signals but do impose a background distortion.

Abstract: The invention provides a computer-implemented method for identifying the type of a vessel of a subject based on Doppler ultrasound data acquired from a subject. The computer-implemented method includes receiving ultrasound data acquired from the subject by way of an ultrasound probe and generating color Doppler ultrasound data from the received ultrasound data. A representation of a vessel of the subject is segmented from the color Doppler ultrasound data and pulse wave Doppler ultrasound data is obtained from the segmented vessel representation. A feature extraction algorithm is then applied to the pulse wave Doppler ultrasound data, thereby extracting a feature of the flow within the segmented vessel and the type of the vessel is identified as an artery or a vein based on the extracted feature of the flow.