A method of identifying coronary artery disease comprising: receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient; analysing the contrast cardiac CT data using machine learning to identify a plurality of centreline seed points in the contrast cardiac CT data predicted to correspond to locations on centrelines of the cardiac arteries of the patient by: analysing the contrast cardiac CT data using machine learning to identify a plurality of predicted centreline seed points; and determining a plurality of centreline seed points corresponding to predicted locations on centrelines of the coronary arteries using machine learning by predicting from an instant determined centreline seed point a probable direction to a further centreline seed point of the coronary artery, and selecting a predicted centreline seed point from the plurality of predicted centreline seed points using the predicted probable direction to a further centreline seed point of the coronary artery; producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified centreline 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. receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient; analysing the contrast cardiac CT data using machine learning to identify a plurality of centreline seed points in the contrast cardiac CT data predicted to correspond to locations on centrelines of the cardiac arteries of the patient by: analysing the contrast cardiac CT data using machine learning to identify a plurality of predicted centreline seed points; and determining a plurality of centreline seed points corresponding to predicted locations on centrelines of the coronary arteries using machine learning by predicting from an instant determined centreline seed point a probable direction to a further centreline seed point of the coronary artery, and selecting a predicted centreline seed point from the plurality of predicted centreline seed points using the predicted probable direction to a further centreline seed point of the coronary artery; analysing the contrast cardiac CT data using machine learning to identify a plurality of predicted centreline seed points; and determining a plurality of centreline seed points corresponding to predicted locations on centrelines of the coronary arteries using machine learning by predicting from an instant determined centreline seed point a probable direction to a further centreline seed point of the coronary artery, and selecting a predicted centreline seed point from the plurality of predicted centreline seed points using the predicted probable direction to a further centreline seed point of the coronary artery; producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified centreline 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.