Oxford University Innovation Ltd

Ultromics Limited

Deborah Markham

Ross Upton

Andrew Richard Parker

Paul Leeson

Edmund Wilkes

Arian Beqiri

Gary Woodward

Jurriath Azmathi Mumith

An embodiment of the invention provides a system for automatically deriving a parameter of a human heart from ultrasound results. A first neural network is arranged to receive a plurality of echocardiographic images and to classify the images into one of at least a two-chamber view and a four-chamber view. A second neural network is arranged to receive echocardiographic images comprising a two- or four-chamber view and to identify the endocardial border of the left ventricle (LV) for each view. End-systole and end-diastole images are then identified and a parameter such as LV volume, ejection fraction, global longitudinal strain and regional longitudinal strain is calculated.

Assessing heart parameters using neural networks

The present disclosure relates to a system () for scoring motion of a heart wall (). The system () includes an imaging system () operable to acquire a first image () of the heart wall () at a first time and a second image () of the heart wall () at a second time. A processor () is provided to identify a first set of contour data in the first image (); and a second set of contour data in the second image (). The processor () defines at least one element (Ei) representing a cardiac cyclic change in a section of the heart wall () in dependence on the first and second sets of contour data. Each element (Ei) is analysed to generate at least one metric which is compared with a reference data model to score the motion of the corresponding section of the heart wall (). The disclosure also relates to a method of scoring motion of a heart wall (); and a non-transitory computer-readable medium.

Image analysis for scoring motion of a heart wall

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Deborah Markham