Siemens Healthcare Gmbh

Siemens Healthineers Ag

Costin Florian Ciusdel

Puneet Sharma

Lucian Mihai Itu

Tiziano Passerini

Mehmet Akif Gulsun

Ingo Schmuecking

Alexandru Turcea

Paul Klein

A method includes processing at least one input dataset (using a multi-level processing algorithm, one or more of the at least one input dataset comprising imaging data of an echocardiography of a cardiovascular system of a patient. The multi-level processing algorithm comprises a multi-task level and a consolidation-task level. An input of the consolidation-task level is coupled to an output of the multi-task level. The multi-task level is configured to determine multiple diagnostic metrics of the cardiovascular system based on the at least one input dataset. The consolidation-task level is configured to determine a fitness of the cardiovascular system of the patient.

Cardiac function assessment using machine-learning algorithms

A first sequence of cardiac image frames are received by a first neural network of the neural network system. The first neural network outputs a first set of feature values. The first set of feature values includes a plurality of data subsets, each corresponding to a respective image frame and relating to spatial features of the respective image frame. The first set of feature values are received at a second neural network of the neural network system. The second neural network outputs a second set of feature values relating to temporal features of the spatial features. Based on the second set of feature values, a cardiac phase value relating to a cardiac phase associated with a first image frame is determined.

Processing image frames of a sequence of cardiac images

A computer-implemented method for personalized assessment of a subject's bone health includes extracting a plurality of features of interest from non-invasive subject data, medical images of the subject, and subject-specific bone turnover marker values. A surrogate model and the plurality of features of interest are used to predict one or more subject-specific measures of interest related to bone health. Then, a visualization of the one or more subject-specific measures of interest related to bone health is generated.

Personalized assessment of bone health

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Azuga, Romania

Costin Florian Ciusdel