Abstract: A method and corresponding system of identifying coronary artery disease. The method comprises receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient, and analysing the contrast cardiac CT data using machine learning to identify a plurality of seed points in the contrast cardiac CT data expected to correspond to locations in cardiac arteries of the patient. The method also comprises producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified seed points, analysing the transverse image slice data using machine learning to produce inner artery wall data and outer artery wall data indicative of predicted respective inner and outer walls of the coronary arteries of the patient, and identifying presence of coronary artery disease using the predicted inner and/or outer walls of the coronary arteries of the patient.

Abstract: Methods and corresponding systems of identifying coronary artery disease. The methods comprises receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient, and analysing the contrast cardiac CT data using machine learning to identify a plurality of seed points in the contrast cardiac CT data expected to correspond to locations in cardiac arteries of the patient. The methods also comprise producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified seed points, analysing the transverse image slice data using machine learning to produce inner artery wall data and outer artery wall data indicative of predicted respective inner and outer walls of the coronary arteries of the patient, and identifying presence of coronary artery disease using the predicted inner and/or outer walls of the coronary arteries of the patient.

Abstract: A coronary artery disease (CAD) analysis system is disclosed that comprises a CAD analysis device and a user interface. The CAD analysis device is arranged to analyse received patient CT scan data and produce CAD analysis data indicative of presence and characterisation of coronary artery disease in the patient CT scan data. The CAD analysis data is indicative of at least one individual stenosis lesion on a coronary artery and a characterisation of the stenosis lesion, and the CAD analysis system is arranged to identify a start location and an end location of the individual stenosis lesion on the coronary artery. The user interface displays a model of coronary arteries of a patient based on the patient CT scan data, and the model is configured to visually indicate an individual stenosis lesion and the individual stenosis lesion characterisation to a user so that the user is able to identify presence and characterisation of each individual stenosis lesion based on the visual indication.

Abstract: A coronary artery disease (CAD) analysis system is disclosed that comprises a CAD analysis device and a user interface. The CAD analysis device is arranged to analyse received patient CT scan data and produce CAD analysis data indicative of presence and characterisation of coronary artery disease in the patient CT scan data. The CAD analysis data is indicative of at least one individual stenosis lesion on a coronary artery and a characterisation of the stenosis lesion, and the CAD analysis system is arranged to identify a start location and an end location of the individual stenosis lesion on the coronary artery. The user interface displays a model of coronary arteries of a patient based on the patient CT scan data, and the model is configured to visually indicate an individual stenosis lesion and the individual stenosis lesion characterisation to a user so that the user is able to identify presence and characterisation of each individual stenosis lesion based on the visual indication.

Abstract: A method and corresponding system of identifying coronary artery disease. The method comprises receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient, and analysing the contrast cardiac CT data using machine learning to identify a plurality of seed points in the contrast cardiac CT data expected to correspond to locations in cardiac arteries of the patient. The method also comprises producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified seed points, analysing the transverse image slice data using machine learning to produce inner artery wall data and outer artery wall data indicative of predicted respective inner and outer walls of the coronary arteries of the patient, and identifying presence of coronary artery disease using the predicted inner and/or outer walls of the coronary arteries of the patient.

Abstract: A method and corresponding system of identifying coronary artery disease. The method comprises receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient, and analysing the contrast cardiac CT data using machine learning to identify a plurality of seed points in the contrast cardiac CT data expected to correspond to locations in cardiac arteries of the patient. The method also comprises producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified seed points, analysing the transverse image slice data using machine learning to produce inner artery wall data and outer artery wall data indicative of predicted respective inner and outer walls of the coronary arteries of the patient, and identifying presence of coronary artery disease using the predicted inner and/or outer walls of the coronary arteries of the patient.

Abstract: A method of automatically determining a calcium score for at least one coronary artery is disclosed. The method comprises receiving cardiac non-contrast CT data indicative of a cardiac non-contrast CT scan carried out on a patient, analysing the cardiac non-contrast CT data in a calcified components identifier to detect candidate coronary artery calcified components, and analysing cardiac non-contrast CT data associated with the candidate coronary artery calcified components using a radiomics analyser to determine radiomic characteristics of the candidate coronary artery calcified components.