An abdominal aortic aneurysm is a common condition in people over 65. It occurs as an abnormal dilatation of part of the wall of the abdominal aorta, a large blood vessel that carries blood to the abdominal organs. The dilatation, known as an aneurysm, makes the wall of the abdominal aorta more fragile; it may rupture, causing severe haemorrhaging that is fatal in 85-90% of cases. According to the guidelines, the manual measurement of the dilatation of the abdominal aortic aneurysm carried out by the physician on the basis of CT images is the main parameter for defining the treatment in order to avoid aneurysm rupture. A reported CT dilatation of more than 5.5 cm for men and 5 cm for women defines the indication for surgery. However, the data provided by the CT scan are not fully used for determining the risks and critical issues associated with aneurysm rupture. Clinical practice has shown that the size of the dilatation by itself, ignoring the other data provided by the CT scan such as the physical characteristics of the tissue of the aorta wall (elasticity, curvature, blood flow) is not a reliable parameter for determining the risk of aneurysm rupture.
By exploiting complex image analysis techniques, data science and models belonging to fluid dynamics and structural analysis, CT data are automatically aggregated and analysed to construct the geometry of the patient’s abdominal aorta in seconds, and simulate:
- the blood flow within the vessel
- the dynamic displacement of the aortic wall during heartbeats and the pressure changes caused by the flow of blood within the vessel
- the pressures and strains exerted on the wall of the abdominal aorta.
In combination with the size of the aneurysm dilatation, these data provide the physicians with an indicator of the likelihood of aneurysm rupture risk and a prediction of future disease progression.
Based on this data, the physicians can decide, illustrate and motivate their intervention strategy to the patient, improving not only the therapeutic decision making process but also the physician-patient relationship.
In combination with the size of the aneurysm dilatation, these data provide the physician with an indicator of the likelihood of aneurysm rupture risk and a prediction of future disease progression.
Based on this data, the physician can decide, illustrate and motivate their intervention strategy to the patient, improving not only the therapeutic decision making process but also the physician-patient relationship.
- Optimisation of CT information, thanks to automatic and fast data aggregation and analysis
- Prediction of disease progression, based on simulations performed on aggregated CT image data
- Physician decision support, from a personalised medicine perspective based on objective data and physician experience