Abstract: A computer-implemented method is provided for predicting a growth rate of an aortic aneurysm. The method comprises analysing one or more geometric measures of a volumetric model of at least a portion of an aorta having an aneurysm. The method further comprises determining, from the analysis, a growth rate prediction for the aortic aneurysm. Computer-readable media and apparatuses are also described.

Abstract: The present invention relates to aortic aneurysm growth progression and predicting future progression of abdominal aortic aneurysms. The present invention concerns the use of at least one protein selected from at least one of four groups of proteins as a biomarker for determining a risk value of abdominal aortic aneurysm future growth for a subject. The groups are: a group of proteins determined to be present at higher concentrations in subjects showing fast abdominal aortic aneurysm growth compared with subjects showing slow abdominal aortic aneurysm growth; a group of proteins determined to be significantly lower in the systemic circulation of subjects following abdominal aortic aneurysm surgery; a group of proteins determined to be present in thrombus of an abdominal aortic aneurysm; and a group of proteins determined to be present in supernatant of an extracted thrombus sample.

Abstract: A method for training a machine learning image segmentation algorithm to segment structural features of a blood vessel in a computed tomography (CT) image is described herein. The method comprises receiving a labelled training set for the machine learning image segmentation algorithm. The labelled training set comprising a plurality of CT images, each CT image of the plurality of CT images showing a targeted region of a subject, the targeted region including at least one blood vessel. The labelled training set further comprises a corresponding plurality of segmentation masks, each segmentation mask labelling at least one structural feature of a blood vessel in a corresponding CT image of the plurality of CT images.

Abstract: Methods for training an algorithm to identify structural anatomical features, for example of a blood vessel, in a non-contrast computed tomography (NCT) image are described herein. The algorithm may comprise an image segmentation algorithm, a random forest classifier, or a generative adversarial network in examples described herein. In one embodiment, a method comprises receiving a labelled training set for a machine learning image segmentation algorithm. The labelled training set comprising a plurality of NCT images, each NCT image of the plurality of NCT images showing a targeted region of a subject, the targeted region including at least one blood vessel. The labelled training set further comprises a corresponding plurality of segmentation masks, each segmentation mask labelling at least one structural feature of a blood vessel in a corresponding NCT image of the plurality of NCT images.

Abstract: A method of characterizing a sample comprising a thrombus collected from a patient is described. The method comprises illuminating the sample with electromagnetic light, detecting light reflected or Raman scattered by the sample at a plurality of wavelengths, and classifying the sample based on a spectral analysis of the detected reflected or Raman scattered light. The spectral analysis may comprise principal component analysis PCA, discriminant function analysis DFA, or cluster analysis. The classification may be according to thrombus color, i.e., erythrocyte rich “red” versus erythrocyte poor “white”, or according to degree of a clinical marker, e.g., microvascular obstruction MVO.

Abstract: A composition for use in the treatment of a pseudo aneurysm in a subject, said composition comprising: i) microbubbles; ii) a magnetic material; and iii) a blood clotting agent; said treatment comprising administering said composition directly into the pseudo aneurysm and applying a magnetic field to the pseudo aneurysm so as to retain the blood clotting agent within the pseudo aneurysm for a prolonged time period compared to a case where no magnetic field is applied to the pseudo aneurysm.

Abstract: The present disclosure relates to a method for determining a risk factor indicative of predicted growth of an abdominal aortic aneurysm of a patient. The method comprises receiving a value representative of a size of the abdominal aortic artery; and a flow-mediated vasodilation or flow-mediated constriction value of an artery of the patient (hereinafter “FMD/C value”). The method also comprises determining a risk factor indicative of predicted growth by evaluating the received values with those in an aneurysm risk model. The aneurysm risk model relates to a risk value indicative of predicted growth of an abdominal aortic aneurysm for a given value representative of the size of an abdominal aortic artery and a given FMD/C value.