Abstract: Proposed concepts aim to provide schemes, solutions, concepts, designs, methods, and systems pertaining to cardiac monitoring of a subject which involves obtaining both echocardiogram data and a cardiac activity signal. In particular, embodiments aim to provide a method of cardiac monitoring in which a cardiac rhythm abnormality value is determined and used to generate supplementary data which can accompany the echocardiogram data to provide a comprehensive view of the cardiac behaviour of the subject during a monitoring time period.

Abstract: The present disclosure provides concepts for searching a medical database comprising electrocardiogram (ECG) data. The method includes obtaining a database comprising a plurality of subject entries, each corresponding to a historic subject and comprising recorded metadata describing characteristics of the respective subject and a recorded vector embedding representing ECG data of the respective subject. A query vector embedding representing ECG data of a query subject is generated with an ECG encoder. The recorded metadata of the plurality of subject entries is compared with query metadata describing characteristics of the query subject, and the recorded vector embeddings are compared with the query vector embedding. One or more similar subject entries are identified based on a result of the comparison.

Abstract: Images are synthesized from a source to a target nature through unsupervised machine learning (ML), based on an original training set of unaligned source and target images, by training a first ML architecture through an unsupervised first learning pipeline applied to the original set, to generate a first trained model and induced target images consisting in representations of original source images compliant with the target nature. A second ML architecture is trained through a supervised second learning pipeline applied to an induced training set of aligned image pairs, each including first and second items corresponding respectively to an original source image and the induced target image associated with the latter, to generate a second trained model enabling image syntheses from the source to the target nature. Also, applications to effective medical image translations.

Abstract: A method for generating a 3D image of a human body part including: train a generative network based on a library of human body part 3D scans of reference to obtain a generative model; make a 3D scan of a studied human body part; define a subset of the studied 3D scan by excluding the content of an area; optimize a latent variable for minimizing a distance between the defined subset and an image of a subset generated by the generative model from the latent variable; generate a complete 3D image of the studied human body part with the generative model using the optimized latent variable.

Abstract: Images are synthesized from a source to a target nature through unsupervised machine learning (ML), based on an original training set of unaligned source and target images, by training a first ML architecture through an unsupervised first learning pipeline applied to the original set, to generate a first trained model and induced target images consisting in representations of original source images compliant with the target nature. A second ML architecture is trained through a supervised second learning pipeline applied to an induced training set of aligned image pairs, each including first and second items corresponding respectively to an original source image and the induced target image associated with the latter, to generate a second trained model enabling image syntheses from the source to the target nature. Also, applications to effective medical image translations.